1. Speech Summarization Sameer R. Maskey

2. 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]

3. 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

4. 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

5. Are Speech and Text Summarization similar? Yes Identifying important information Some lexical, discourse features Extraction NO! Speech Signal Prosodic features NLP tools? Segments – sentences? Generation? ASR transcripts Data size

6. Text vs. Speech Summarization (NEWS) Speech Signal Speech Channels - phone, remote satellite, station Transcripts - ASR, Close Captioned Many Speakers - speaking styles Prosodic Features -pitch, energy, duration Structure -Anchor, Reporter Interaction Commercials, Weather Report Transcript- Manual Some Lexical Features Story presentation style Error-free Text Lexical Features Segmentation -sentences NLP tools

7. Speech Summarization (NEWS) Speech Signal Speech Channels - phone, remote satellite, station Transcripts - ASR, Close Captioned Many Speakers - speaking styles Prosodic Features -pitch, energy, duration Structure -Anchor, Reporter Interaction Commercials, Weather Report Transcript- Manual Some Lexical Features Story presentation style Error-free Text Lexical Features Segmentation -sentences many NLP tools

8. 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

9. Extraction Training w/ manual Summaries Sentence Extraction Similarity Measures Concept Level Extract concepts units Generate Words/Phrases Use of Structured Data SOME SUMMARIZATION TECHNIQUES BASED ON TEXT (LEXICAL FEATURES) [Salton, et al., 1995] [McKeown, et al., 2001] [Hovy & Lin, 1999] [Witbrock & Mittal, 1999] [Maybury, 1995]

10. 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

11. “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

12. Text relationship map [Salton, et al., 1995]

13.  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] Where S is summary given k features Fj and P(Fj) & P(Fj|s of S) can be computed by counting occurrences Summarization by feature based statistical models [Kupiec, et al., 1995]

14. 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

15. 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 Link analysis (semantic relations within the structure) Domain dependent importance of events

16. 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

17. Word scoring with dependency structure Sentence extraction with similarity measures Classification User access information Weighted finite state transducers Removing disfluencies SPEECH SUMMARIZATION TECHNIQUES [Christensen et al., 2004] [Hori C. et al., 1999, 2002], [Hori T. et al., 2003] [Koumpis & Renals, 2004] [He et al., 1999] [Hori T. et al., 2003] [Zechner, 2001]

18. 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  Using position features to find the relevant nearby sentences after detecting the topic sentence where Sim is a similarity measure between two sentences

19. 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 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 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 HCLGSD Speech recognizerTranslator Speech Translator

20. 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)

21. 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) 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

22. Assumptions There are a few assumptions made in the previously mentioned methods  Segmentation  Information Extraction  Automatic Speech Recognition  Manual Transcripts  Annotation

23. Speech Segmentation? segmentation speech text Extraction Features Techniques Evaluation Sentences Topics Text Retrieval Methods on ASR Transcripts Segmentation  Sentences  Stories  Topic  Speaker

24. Information Extraction from Speech Data? segmentation speech text Extraction Features Techniques Evaluation Sentences Topics Text Retrieval Methods on ASR Transcripts Information Extraction  Named Entities  Relevant Sentences and Topics  Weather/Sports Information

25. Audio segmentation Audio Segmentation TopicsStory Commercials Sentences Weather Speaker Gender Speaker Types

26. 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

27. 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

28. 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 SourcePrecisionRecall ABC9094 CNN9575 Jim Lehrer Show7752

29. 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

30. 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]

31. 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= and model using 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 Precision and recall of 89% for manual transcript with explicit modeling

32. Named entity extraction results [Miller, et al., 1999] BBN Named Entity Performance as a function of WER [Miller, et al., 1999]

33. 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

34. 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

35. Speech Summarization (NEWS) Speech Signal Speech Channels - phone, remote satellite, station Transcripts - ASR, Close Captioned Many Speakers - speaking styles Prosodic Features -pitch, energy, duration Structure -Anchor, Reporter Interaction Commercials, Weather Report Transcript- Manual Some Lexical Features Story presentation style Error-free Text Lexical Features Segmentation -sentences many NLP tools ACOUSTIC LEXICAL DISCOURSE STRUCTURAL

36. Speech Summarization INPUT: Transcripts + ACOUSTICLEXICALDISCOURSESTRUCTURAL /Sentence Segmentation, Speaker Identification, Speaker Clustering, Manual Annotation, 2-Level Summary  Headlines  Summary Story Named Entity Detection, POS tagging

37. 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

38. 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)

39. 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

40. Annotations - dLabel v2.5

41. 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

42. Annotations – Web Interface

44. 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

45. 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

46. Acoustic Features – Praat: Extraction Tool

47. 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

48. 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

49. 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

50. Discourse Feature Given-New Feature Value Computed using the following equation 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) 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

51. 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

52. 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

53. Classifiers Choice of available classifier may affect the comparison of feature sets Compared 4 different classifiers by plotting threshold (ROC) curve and computing Area Under Curve (AOC) Best Classifier has AOC of 1 ClassifierAOC Bayesian Network 0.771 C4.5 Decision Trees 0.647 Ripper 0.643 Support Vector Machines 0.535

54. ROC Curves

55. 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. PrecisionRecallF-Measure Baseline0.4300.429 L+S+A+D0.4890.6130.544 F-Measure is 11.5% higher than the baseline.

56. 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

57. Baseline and the Best F-measure

58. F-Measure for All 15 Feature Sets

59. 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

60. 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)

61. Evaluation using ROUGE metric ROUG E-1 ROUG E-2 ROUG E-3 ROUG E-4 ROUG E-L ROUG E-S ROUG E-SU Baseline0.580.510.500.490.570.400.41 L+S+A+D0.840.810.800.790.840.76 In average L+S+A+D is 30.3% higher than the baseline

62. Results - ROUGE

63. 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

64. Is It Possible to Build ‘good’ Automatic Speech Summarization Without Any Transcripts? Feature SetF-MeasureROUGE-avg L+S+A+D0.540.80 L0.490.70 S+A0.490.68 A0.470.63 Baseline0.430.50 Just using A+S without any lexical features we get 6% higher F- measure and 18% higher ROUGE-avg than the baseline

65. 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 RankTypeFeature 1ATime Length in seconds 2LNum. of words 3LTot. Named Entities 4SNormalized Sent. Pos 5DGiven-New Score

66. 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

67. 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

68. SPEECH ASR MANUAL INPUT SGML Parser XML Parser Transcript Aligner Sentence Parser Text PreProcess or PRE-PROCESSING FEATURE EXTRACTION ACOUSTIC LEXICAL STRUCTURE DISCOURCE SEGMENTATION/ DETECTION SEGMENTATI ON Story Headline Interviews Q&A DETECTION SoundBites SoundBite- Speaker Signon/off Weather forecast Named-Entity Tagger SUMMARIZERSUMMARIZER MIN ING MODEL/PRESENTAT ION SPEECH MAN- UAL ASR CHUNKS CC Summarization Architecture

69. 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 Generation or Extraction?

70. 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