Speech Summarization by t36hu9Hl

VIEWS: 2 PAGES: 70

									Speech
Summarization


 Sameer R. Maskey
Summarization

   „the process of distilling the most important
    information from a source (or sources) to produce
    an abridged version for a particular user (or users)
    and task (or tasks) [Mani and Maybury, 1999]
Indicative or Informative
   Indicative
       Suggests contents of the document
       Better suits for searchers

   Informative
       Meant to represent the document
       Better suits users who want the overview
Speech Summarization
   Speech summarization entails „summarizing‟
    speech
       Identify important information relevant to users and
        the story
       Represent the important information
       Present the extracted/inferred information as an
        addition or substitute to the story
Are Speech and Text
Summarization similar?
   Yes                          NO!
   Identifying important        Speech Signal
    information                  Prosodic features
   Some lexical, discourse      NLP tools?
    features                     Segments – sentences?
   Extraction                   Generation?
                                 ASR transcripts
                                 Data size
Text vs. Speech Summarization
(NEWS)          Speech Signal

                                           Speech Channels
                                           - phone, remote satellite, station

   Error-free Text                                Transcripts
                      Transcript- Manual          - ASR, Close Captioned

Lexical Features                                      Many Speakers
                     Some Lexical Features
                                                      - speaking styles

 Segmentation          Story presentation         Structure
 -sentences            style                      -Anchor, Reporter Interaction

                                              Prosodic Features
       NLP tools                              -pitch, energy, duration

                                    Commercials, Weather Report
 Speech Summarization (NEWS)
                                  Speech Signal

                                         Speech Channels
                                         - phone, remote satellite, station

                                                Transcripts
 Error-free Text    Transcript- Manual          - ASR, Close Captioned

Lexical Features                                    Many Speakers
                   Some Lexical Features
                                                    - speaking styles

 Segmentation        Story presentation         Structure
 -sentences          style                      -Anchor, Reporter Interaction

                                            Prosodic Features
           many NLP tools                   -pitch, energy, duration

                                  Commercials, Weather Report
Why speech summarization?

    Multimedia production and size are increasing: need less time-
     consuming ways to archive, extract, use and browse speech
     data - speech summarization, a possible solution
    Due to temporal nature of speech, difficult to scan like text
    User-specific summaries of broadcast news is useful
    Summarizing voicemails can help us better organize
     voicemails
               [Salton, et al., 1995]

                  Sentence Extraction
                  Similarity Measures
                                           [McKeown, et al., 2001]

                                           Extraction Training
                                          w/ manual Summaries
   SOME SUMMARIZATION
                                                              [Hovy & Lin, 1999]
    TECHNIQUES BASED
ON TEXT (LEXICAL FEATURES)                             Concept Level
                                                    Extract concepts units

                                                   [Witbrock & Mittal, 1999]

                                             Generate Words/Phrases


                                            [Maybury, 1995]

                                        Use of Structured Data
Summarization by sentence extraction
with similarity measures [Salton, et al., 1995]


   Many present day techniques involve sentence extraction
   Extract sentence by finding similar sentence to topic sentence
    or dissimilar sentences to already built summary (Maximal
    Marginal Relativity)
   Find sentences similar to the topic sentence
   Various similarity measures [Salton, et al., 1995]
     Cosine Measure
     Vocabulary Overlap
     Topic words overlap
     Content Signatures Overlap
“Automatic text structuring and summarization”
[Salton, et al., 1995]


     Uses hypertext link generation to summarize documents
     Builds intra-document hypertext links
     Coherent topic distinguished by separate chunk of links
     Remove the links that are not in close proximity
     Traverse along the nodes to select a path that defines a summary
     Traverse order can be
          Bushy Path: constructed out n most bushy nodes
          Depth first Path: Traverse the most bushy path after each node
          Segmented bushy path: construct bushy paths individually and connect
           them on text level
Text relationship map [Salton, et al., 1995]
Summarization by feature based statistical
models [Kupiec, et al., 1995]

     Build manual summaries using available number of annotators
     Extract set of features from the manual summaries
     Train the statistical model with the given set of values for manual
      summaries
     Use the trained model to score each sentence in the test data
     Extract ‘n’ highest scoring sentences
     Various statistical models/machine learning
          Regression Models
          Various classifiers
          Bayes rules for computing probability for inclusion by counting [Kupiec, et
           al., 1995]                          k

                                                         P( F
                                                        j 1
                                                                 j   |s  S ) P( s  S )
                        P( s  S | F1 , F2, ...Fk )                 k

                                                                P( F )
                                                                 j 1
                                                                            j


       Where S is summary given k features Fj and P(Fj) & P(Fj|s of S) can be
        computed by counting occurrences
Summarization by concept/content level
extraction and generation                            [Hovy & Lin, 1999] , [Witbrock &
Mittal, 1999]

     Quite a few text summarizers based on extracting concept/content
      and presenting them as summary
          Concept Words/Themes]
          Content Units [Hovy & Lin, 1999]
          Topic Identification
      [Hovy & Lin, 1999] uses Concept Wavefront to build concept
      taxonomy
          Builds concept signatures by finding relevant words in 30000 WSJ documents each
           categorized into different topics
     Phrase concatenation of relevant concepts/content
     Sentence planning for generation
Summarization of Structured text database
[Maybury, 1995]


    Summarization of text represented in a structured
     form: database, templates
        Report generation of a medical history from a database is such an
         example

                                                           s
                                          # of occurrence of event E
               Relative frequencyof E 
                                              Total # of all events

    Link analysis (semantic relations within the
     structure)
    Domain dependent importance of events
Speech summarization: present

   Speech Summarization seems to be mostly based on extractive
    summarization
   Extraction of words, sentences, content units
   Some compression methods have also been proposed
   Generation as in some text-summarization techniques is not
    available/feasible
       Mainly due to the nature of the content
                      [Christensen et al., 2004]

                 Sentence extraction with
                   similarity measures
                                                   [Hori C. et al., 1999, 2002] , [Hori T. et al., 2003]

                                                           Word scoring
                                                     with dependency structure
SPEECH SUMMARIZATION
     TECHNIQUES                                                [Koumpis & Renals, 2004]

                                                                        Classification

                                                        [He et al., 1999]

                                                         User access information
        [Zechner, 2001]

                                                       [Hori T. et al., 2003]
         Removing disfluencies
                                                    Weighted finite state
                                                        transducers
Content/Context sentence level extraction for
speech summary [Christensen et al., 2004]


    These are commonly used speech summarization techniques:
       finding sentences similar to the lead topic sentences
                       ^
                  Sk  s  arg max {Sim( s1 , si )}
                              si D / E

       Using position features to find the relevant nearby sentences
        after detecting the topic sentence
                          ^
                    Sk  s  arg max {Sim( D, si )}
                                 si D / E

               where Sim is a similarity measure between two
                sentences
Weighted finite state transducers for speech
summarization               [Hori T. et al., 2003]


     Speech Summarization includes speech recognition, paraphrasing,
      sentence compaction integrated into single Weighted Finite State
      Transducer                          R  H C  LG
     Enables decoder to employ all the knowledge sources in one-pass
      strategy
     Speech recognition using WFST
       Where H is state network of triphone HMMs, C is triphone connection rules, L is pronunciation and
         G is trigram language model
     Paraphrasing can be looked at as a kind of machine translation with
      translation probability P(W|T) where W is source language and T is the
      target language               Z  H C  LG S  D
     If S is the WFST representing translation rules and D is the language
      model of the target language speech summarization can bee looked at as
      the following composition Speech Translator

                               H       C         L         G             S       D
                                Speech recognizer                   Translator
User access information for finding salient
parts       [He et al., 1999]




   Idea is to summarize lectures or shows extracting the parts that have
    been viewed the longest
   Needs multiple users of the same show, meeting or lecture for a
    statistically significant training data
   For summarizing lectures compute the time spent on each slide
   Summarizer based on user access logs did as well as summarizers
    that used linguistic and acoustic features
        Average score of 4.5 on a scale of 1 to 8 for the summarizer (subjective evaluation)
Word level extraction by scoring/classifying words
[Hori C. et al., 1999, 2002]



        Score each word in the sentence and extract a set of words to form
         a sentence whose total score is the product/sum of the scores of
         each word
        Example:
           Word Significance score (topic words)
           Linguistic Score (bigram probability)
           Confidence Score (from ASR)
           Word Concatenation Score (dependency structure grammar)
                          M
                 S (V )  {L(vm | ... vm 1 )  I I (vm )  cC (vm )  T Tr (vm1,vm )
                          m 1


           Where M is the number of words to be extracted, and I C T
            are weighting factors for balancing among L, I, C, and T r
Assumptions
   There are a few assumptions made in the
    previously mentioned methods
       Segmentation
       Information Extraction
       Automatic Speech Recognition
       Manual Transcripts
       Annotation
      Speech Segmentation?
     Segmentation
         Sentences
         Stories
         Topic
         Speaker
•Sentences                                       •Features
•Topics           speech                         •Techniques
                                                 •Evaluation
             segmentation
                         Extraction   text
                                             •Text Retrieval Methods
                                              on ASR Transcripts
  Information Extraction from
  Speech Data?
     Information Extraction
         Named Entities
         Relevant Sentences and Topics
         Weather/Sports Information

•Sentences                                       •Features
•Topics           speech                         •Techniques
                                                 •Evaluation
             segmentation
                         Extraction   text
                                             •Text Retrieval Methods
                                              on ASR Transcripts
Audio segmentation


                    Audio Segmentation



 Topics   Story   Sentences


                               Commercials   Weather
 Speaker Speaker    Gender
 Types
Audio segmentation methods


    Can be roughly categorized in two different categories
        Language Models [Dharanipragada, et al., 1999] , [Gotoh & Renals,
         2000], [Maybury, 1998], [Shriberg, et al., 2000]
        Prosody Models [Gotoh & Renals, 2000], [Meinedo & Neto, 2003] ,
         [Shriberg, et al., 2000]
    Different methods work better for different purposes and different styles
     of data [Shriberg, et al., 2000]
    Discourse cues based method highly effective in broadcast news
     segmentation [Maybury, 1998]
    Prosodic model outperforms most of the pure language modeling
     methods [Shriberg, et al., 2000], [Gotoh & Renals, 2000]
    Combined model of using NLP techniques on ASR transcripts and
     prosodic features seem to work the best
Overview of a few algorithms:
statistical model [Gotoh & Renals, 2000]

    Sentence Boundary Detection: Finite State Model that extracts boundary
     information from text and audio sources
    Uses Language and Pause Duration Model
    Language Model: Represent boundary as two classes with ‚last word‛ or ‚not last
     word‛
    Pause Duration Model:
         Prosodic features strongly affected by word
    Two models can be combined
    Prosody Model outperforms language model
    Combined model outperforms both
Segmentation using discourse cues [Maybury,
1998]


       Discourse Cues Based Story Segmentation
       Sentence segmentation is not possible with this method
       Discourse Cues in CNN
           Start of Broadcast
           Anchor to Reporter Handoff, Reporter to Anchor Handoff
           Cataphoric Segment (still ahead of this news)
           Broadcast End
       Time Enhanced Finite State Machine to represent discourse states such as anchor,
        reporter, advertisement, etc
       Other features used are named entities, part of speech, discourse shifts ‚>>‛
        speaker change, ‚>>>‛ subject change
             Source                 Precision             Recall
             ABC                    90                    94
             CNN                    95                    75
             Jim Lehrer Show        77                    52
Speech Segmentation


    Segmentation methods essential for any kind of extractive speech
     summarization
    Sentence Segmentation in speech data is hard
    Prosody Model usually works better than Language Model
    Different prosody features useful for different kinds of speech
     data
    Pause features essential in broadcast news segmentation
    Phone duration essential in telephone speech segmentation
    Combined linguistic and prosody model works the best
Information Extraction from Speech

     Different types of information need to be extracted depending on the
      type of speech data
     Broadcast News:
         Stories [Merlino, et al., 1997]
         Named Entities [Miller, et al., 1999] , [Gotoh & Renals, 2000]
         Weather information
     Meetings
         Main points by a particular speaker
         Address
         Dates
     Voicemail
         Phone Numbers [Whittaker, et al., 2002]
         Caller Names [Whittaker, et al., 2002]
Statistical model for extracting named entities
[Miller, et al., 1999] , [Gotoh & Renals, 2000]



     Statistical Framework: V denote vocabulary and C set of name classes,
             Modeling class information as word attribute: Denote e=<c, w> and model
              using
                              p ( e1,..., em )     p(e | e ,... e
                                                   i 1.. m
                                                              i   1      )
                                                                      i 1


             In the above equation ‘e’ for two words with two different classes are
              considered different. This bring data sparsity problem
             Maximum likelihood estimates by frequency counts
             Most probable sequence of class names by Viterbi algorithm
                             
                       c1 ...cm  arg max p( c, w 1,..., c, w m )
                                          c1..cm

             Precision and recall of 89% for manual transcript with explicit modeling
Named entity extraction results                [Miller, et al., 1999]




    BBN Named Entity Performance as a function of WER [Miller, et al., 1999]
Information Extraction from
Speech

    Information Extraction from speech data essential tool for speech
     summarization
    Named Entities, phone number, speaker types are some
     frequently extracted entities
    Named Entity tagging in speech is harder than in text because
     ASR transcript lacks punctuation, sentence boundaries,
     capitalization, etc
    Statistical models perform reasonably well on named entity
     tagging
Speech Summarization at
Columbia
   We make a few assumptions in segmentation and
    extraction
   Some new techniques proposed
   2-level summary
       Headlines for each story
       Summary for each story

   Summarization Client and Server model
 Speech Summarization (NEWS)
                                    Speech Signal

 ACOUSTIC                                  Speech Channels
                                           - phone, remote satellite, station

                                                  Transcripts
   Error-free Text    Transcript- Manual          - ASR, Close Captioned
  LEXICAL
 Lexical Features                                     Many Speakers
                     Some Lexical Features
                                                      - speaking styles

  Segmentation
DISCOURSE              Story presentation         Structure
  -sentences           style                      -Anchor, Reporter Interaction

                                              Prosodic Features
         many NLP tools
STRUCTURAL                                    -pitch, energy, duration

                                    Commercials, Weather Report
Speech Summarization
INPUT:
                              +       Transcripts




  ACOUSTIC              LEXICAL           DISCOURSE          STRUCTURAL

Story/Sentence Segmentation, Speaker Identification, Speaker Clustering,
Manual Annotation, Named Entity Detection, POS tagging



                          2-Level Summary
                             Headlines
                                 Summary
Corpus
   Topic Detection and Tracking Corpus (TDT-2)
   We are using 20 “CNN Headline shows” for
    summarization
   216 stories in total
   10 hours of speech data
   Using Manual transcripts, Dragon and BBN ASR
    transcripts
Annotations - Entities
   We want to detect –
       Headlines
       Greetings
       Signoff
       SoundByte
       SoundByte-Speaker
       Interviews

   We annotated all of the above entities and the named entities
    (person, place, organization)
Annotations – by Whom and
How?
   We created a labeling manual following ACE
    standards
   Annotated by 2 annotators over a course of a year
   48 hours of CNN headlines news in total
   We built a labeling interface dLabel v2.5 that
    went through 3 revisions for this purpose
Annotations - dLabel v2.5
Annotations – „Building
Summaries‟
   20 CNN shows annotated for extractive summary
   A Brief Labeling Manual
   No detailed instruction on what to choose and
    what not to?
   We built a web-interface for this purpose, where
    annotator can click on sentences to be included in
    the summary
   Summaries stored in a MySQL database
Annotations – Web Interface
Annotations – Web Interface
Acoustic Features
   F0 features
        max, min, mean, median, slope
             Change in pitch may be a topic shift
   RMS energy feature
        max, min, mean
             Higher amplitude probably means a stress on the phrases
   Duration
        Length of sentence in seconds (endtime – starttime)
             Very short or a long sentence might not be important for summary
   Speaker Rate
        how fast the speaker is speaking
             Slower rate may mean more emphasis in a particular sentence
Acoustic Features – Problems in
Extraction
   What should be the segment to extract these features –
    sentences, turn, stories?

   We do not have sentence boundaries.

   A dynamic programming aligner to align manual sentence
    boundary with ASR transcripts

   Feature values needs to be normalized by speaker: used
    Speaker Cluster ID available from BBN ASR
Acoustic Features – Praat:
Extraction Tool
Lexical Features
   Named Entities in a sentence
       Person
       People
       Organization
       Total count of named entities

   Num. of words in a sentence
   Num. of words in previous and next sentence
Lexical Features - Issues
   Using Manual Transcript
   Sentence boundary detection using Ratnaparkhi‟s
    mxterminator
   Named Entities annotated
   For ASR transcript:
     Sentence boundaries aligned
     Automatic Named Entities detected using BBN‟s
      Identifinder
     Many NLP tools fail when used with ASR transcript
Structural Features
   Position
       Position of the sentence in the story and the turn
       Turn position in the show

   Speaker Type
       Reporter or Not

   Previous and Next Speaker Type
   Change in Speaker Type
    Discourse Feature
    Given-New Feature Value
    Computed using the following equation
                             ni   si
                     S (i)     
                             d t d
    where n_i is the number of ‘new’ noun stems in sentence i, d is the total
    number of unique nouns, s_i is the number of noun stems that have already
    been seen, t is the total number of nouns

   Intuition:
      „newness‟ ~ more new unique nouns in the sentence (ni/d)
      If many nouns already seen in the sentence ~ higher
       „givenness‟ s_i/(t-d)
Experiments
   Sentence Extraction as a summary
   Binary Classification problem
     „0‟ not in the summary
       „1‟ in the summary
   10 hours of CNN news shows
   4 different sets of features – acoustic, lexical, structural, discourse
   10 fold-cross validation
   90/10 train and test
   4 different classifiers
   WEKA and YALE learning tool
   Feature Selection
   Evaluation using F-Measure and ROUGE metrics
Feature Sets
   We want to compare the various combination of our “4”
    feature sets
     Acoustic/Prosodic (A)
     Lexical (L)
     Structural (S)
     Discourse (D)



   Combinations of feature sets, 15 in total
       L, A, …, L+A, L+S, … , L+A+S, … , L+S+D, … , L+A+S+D
Classifiers
                                   Classifier            AOC
   Choice of available classifier may
    affect the comparison of feature Bayesian Network      0.771
    sets
                                   C4.5 Decision Trees     0.647
   Compared 4 different classifiers
    by plotting threshold (ROC) curve
    and computing Area Under Curve Ripper                  0.643
    (AOC)

                                   Support Vector          0.535
   Best Classifier has AOC of 1
                                   Machines
ROC Curves
                                                              ROC curves for some classifiers


                                          800



                                          700



                                          600
    True Positives (Sensitivity)




                                          500


                                                                                                               bayesNet
                                          400                                                                  decisiontable
                                                                                                               decisiontreeJ48


                                          300



                                          200



                                          100



                                           0
                                   -500         0   500       1000            1500        2000   2500   3000
                                                          False Positives (Specificity)
Results – Best Combined
Feature Set
   We obtained best F-measure for 10 fold cross validation using all acoustic (A),
    lexical (L), discourse (D) and structural (S) feature.


                         Precision           Recall               F-Measure
    Baseline             0.430               0.429                0.429
    L+S+A+D              0.489               0.613                0.544

       F-Measure is 11.5% higher than the baseline.
What is the Baseline?
   Baseline is the first 23% of sentences in each story.
       In Average Model summaries were 23% in length
   In summarization selecting first n% of sentences is pretty standard baseline
   For our purpose this is a very strict baseline, why?
     Because stories are short. In average 18.2 sentences for
      each story
     In broadcast news it is standard to summarize the story in
      the introduction
     These sentences are likely to be in the summary
Baseline and the Best F-measure
            0.700




            0.650




            0.600

                                                     (-bs-)
                                                     LS
                                                     LSD
    Score




            0.550                                    LA
                                                     LAD
                                                     LSA
                                                     LADS

            0.500




            0.450




            0.400
                    Precision   Recall   F-Measure
                                Metric
F-Measure for All 15 Feature Sets
        0.700
                    0.679


                                                                                                                      0.618
        0.600                                                                                                 0.604           0.608 0.613


                            0.550                                                               0.543 0.547
                                                                    0.542       0.542                                     0.540 0.544
                                    0.531                                                                     0.524 0.532
                                                            0.512     0.505       0.511 0.511 0.513
        0.500                                       0.495             0.493 0.495 0.497
                                                          0.478
                                                                0.487 0.481       0.483 0.482 0.483             0.486 0.489
                                                    0.468                                           0.463 0.467
                                                                            0.455
                                                    0.443 0.447 0.443
                                            0.430
                                            0.429
        0.400
                                    0.385
Score




                            0.329                                                                                        Precision
                                                                                                                         Recall
        0.300                       0.302
                                                                                                                         F-Measure


                            0.235
        0.200


                    0.133
        0.100
                    0.074



        0.000
                D       S      SD     (-bs-)    A      AD       L      SA     LD     SAD   LS      LSD   LA      LAD     LSA LADS
                                                                      Feature Sets
Evaluation using ROUGE
   F-measure is a too strict measure
   Predicted summary sentences has to match
    exactly with the summary sentences
   What if we have a predicted sentence that is not
    an exact but has a similar content?
   ROUGE takes account of this
ROUGE metric
   Recall-Oriented Understudy for Gisting Evaluation (ROUGE)
   ROUGE-N (where N=1,2,3,4 grams)
   ROUGE-L (longest common subsequence)
   ROUGE-S (skip bigram)
   ROUGE-SU (skip bigram counting unigrams as well)
Evaluation using ROUGE metric
           ROUG ROUG ROUG          ROUG     ROUG     ROUG   ROUG
           E-1  E-2  E-3           E-4      E-L      E-S    E-SU
Baseline   0.58    0.51    0.50    0.49     0.57     0.40   0.41

L+S+A+D 0.84       0.81    0.80    0.79     0.84     0.76   0.76




    In average L+S+A+D is 30.3% higher than the baseline
          Results - ROUGE
        0.900



        0.800



        0.700



        0.600



        0.500
Score




        0.400



        0.300
                                                                                               ROUGE-S*
                                                                                               ROUGE-SU*

        0.200                                                                                  ROUGE-4
                                                                                               ROUGE-3
                                                                                               ROUGE-2
        0.100                                                                                  ROUGE-L
                                                                                               ROUGE-1

        0.000
                D   S   SD   (-bs-)   AD   A   LD    L      SA     LSD   LS   SAD   LAD   LA     LSA       LADS
                                                    Feature Sets
Does importance of „what‟ is said
correlates with „how‟ it is said?
   Hypothesis: “Speakers change their amplitude, pitch,
    speaking rate to signify importance of words, phrases,
    sentences.”

   If this is the case then the prediction labels for sentences
    predicted using acoustic features (A) should correlate with
    labels predicted using lexical features (L)

   We found correlation of 0.74

   This above correlation is a strong support for our hypothesis
Is It Possible to Build „good‟
Automatic Speech Summarization
Without Any Transcripts?

      Feature Set             F-Measure              ROUGE-avg
      L+S+A+D                 0.54                   0.80
      L                       0.49                   0.70
      S+A                     0.49                   0.68
      A                       0.47                   0.63
      Baseline                0.43                   0.50

   Just using A+S without any lexical features we get 6% higher F-
    measure and 18% higher ROUGE-avg than the baseline
Feature selection
   We used feature selection to find the best feature set among all the
    features in the combined set
   5 best features are shown in the table
   These 5 features consist of all 4 different feature sets
   Feature Selection also selected these 5 features as the optimal feature
    set
   F-measure using just 5 features is 0.53 which only 1% lower than
    using all features


      Rank        Type      Feature
          1          A      Time Length in seconds
          2          L      Num. of words
          3          L      Tot. Named Entities
          4          S      Normalized Sent. Pos
          5          D      Given-New Score
Problems and Future Work
   We assume we have a good
       Sentence boundary detection
       Speaker IDs
       Named Entities


   We obtain a very good speaker IDs and named entities from BBN but no
    sentence boundaries

   We have to address the sentence boundary detection as a problem on its own.

       Alternative solution: We can do a „breath group‟ level segmentation
        and build a model based on such segmentation
More Current and Future Work

   We annotated headlines, greetings, signoffs, interviews,
    soundbytes, soundbyte speakers, interviewees
   We want to detect these entities
       (students involved for detecting some of these entities –
        Aaron Roth, Irina Likhtina)


   We want to present summary and these entities in a
    unified browsable frame work
       (student involved – Lauren Wilcox)

   The browser is implemented in client/server framework
                          Summarization Architecture
                                       SEGMENTATI
                                       ON
             SGML
             Parser                      Story
SPEECH                     ACOUSTIC

                                         Headline        S
              XML
                                                         U
             Parser
                                                         M
                                         Interviews
                                                         M
                           LEXICAL                       A
                                         Q&A
ASR
           Transcript                                    R       SPEECH
            Aligner
                                                         I
                                       DETECTION         Z       MAN-
                                                         E       UAL

CC         Sentence       STRUCTURE                      R
            Parser                       SoundBites              ASR


                                                                 CHUNKS
                                         SoundBite-
                                         Speaker        MIN
              Text
MANUAL
                                                        ING
           PreProcess     DISCOURCE      Signon/off
               or

          Named-Entity                   Weather
            Tagger                       forecast

                           FEATURE
 INPUT   PRE-PROCESSING   EXTRACTION   SEGMENTATION/   MODEL/PRESENTAT
                                         DETECTION           ION
    Generation or Extraction?
   SENT27 a trial that pits the cattle industry against tv talk show host oprah winfrey is under way in
    amarillo , texas.
   SENT28 jury selection began in the defamation lawsuit began this morning .
   SENT29 winfrey and a vegetarian activist are being sued over an exchange on her April 16, 1996 show .
   SENT30 texas cattle producers claim the activists suggested americans could get mad cow disease from
    eating beef .
   SENT31 and winfrey quipped , this has stopped me cold from eating another burger
   SENT32 the plaintiffs say that hurt beef prices and they sued under a law banning false and disparaging
    statements about agricultural products
   SENT33 what oprah has done is extremely smart and there's nothing wrong with it she has moved her
    show to amarillo texas , for a while
   SENT34 people are lined up , trying to get tickets to her show so i'm not sure this hurts oprah .
   SENT35 incidentally oprah tried to move it out of amarillo . she's failed and now she has brought her
    show to amarillo .
   SENT36 the key is , can the jurors be fair
   SENT37 when they're questioned by both sides, by the judge , they will be asked, can you be fair to both
    sides
   SENT38 if they say , there's your jury panel
   SENT39 oprah winfrey's lawyers had tried to move the case from amarillo , saying they couldn't get an
    impartial jury
   SENT40 however, the judge moved against them in that matter …



                    story                                                                 summary
Conclusion
   We talked about different techniques to build
    summarization systems
   We described some speech-specific summarization
    algorithms
   We showed feature comparison techniques for speech
    summarization
     A model using a combination of lexical, acoustic,
      discourse and structural feature is one of the best model
      so far.
     Acoustic features correlate with the content of the
      sentences
   We discussed possibilities of summarizing speech
    without any transcribed text

								
To top