Story Segmentation in English Mandarin and Arabic Broadcast News

1. Story Segmentation in English Mandarin and Arabic Broadcast News Andrew Rosenberg, Julia Hirschberg Columbia University 5/31/06

2. Outline • Introduction • Approach • Motivating Example • Results HLT/NAACL'06 - Practice Talk

3. Why do we need story segmentation? • News shows commonly contain many distinct stories. • Identifying story (topic) boundaries improves: • Summarization [Hearst and Plaunt 93] • Information Retrieval [Hearst and Plaunt 93] • Anaphora Resolution [Kozima 93] HLT/NAACL'06 - Practice Talk

4. Our Approach • Input: Speech signal, ASR transcript, automatic speaker diarization and automatic sentence boundary hypotheses • Assume: story boundaries occur at sentence boundaries • JRip: java implementation of Cohen’s RIPPER, a rule induction algorithm. • build rulesets for each show individually • using Lexical, Acoustic and Speaker-dependant features. HLT/NAACL'06 - Practice Talk

5. Corpus Description • Broadcast News material from TDT4 Corpus distributed by LDC • English: 312.5 hrs, 450 broadcasts 6 shows • Mandarin: 134 hrs, 205 broadcasts, 3 shows • Arabic: 88.5 hrs, 109 broadcasts, 2 shows • Each broadcast contains between 10 and 20 stories HLT/NAACL'06 - Practice Talk

6. Story segmentation example the united states finished at the top with a total of ninety seven medals thirty nine of them gold russian china and australia rounded up its four andrea run economy seemed to champion style welcome home even though she was stripped of her individual gold medal at the sydney olympics in armenian gymnast tested positive for a banned stimulant that was in a nonprescription cold medicine she took from any as government is honoring her with its own gold medal inscribed everlasting olympic champion the international olympic committee did allow run a con to keep her team gold medal and the silver medal she won in the vote compass a spokeswoman says republican senator strom thurmond is going very well after falling ill saturday he spent the night it will to read army medical center in washington HLT/NAACL'06 - Practice Talk

7. Story segmentation example • the united states finished at the top with a total of ninety seven medals thirty nine of them gold russian china and australia rounded up its four • andrea run economy seemed to champion style welcome home even though she was stripped of her individual gold medal at the sydney olympics in armenian gymnast tested positive for a banned stimulant • that was in a nonprescription cold medicine she took from any as government is honoring her with its own gold medal inscribed everlasting olympic champion • the international olympic committee did allow run a con to keep her team gold medal and the silver medal she won in the vote compass a spokeswoman says republican senator strom thurmond is doing very well after falling ill saturday • he spent the night it will to read army medical center in washington HLT/NAACL'06 - Practice Talk

8. Story segmentation example • the united states finished at the top with a total of ninety seven medals thirty nine of them gold russian china and australia rounded up its four • andrea run economy seemed to champion style welcome home even though she was stripped of her individual gold medal at the sydney olympics ***in armenian gymnast tested positive for a banned stimulant • --- that was in a nonprescription cold medicine she took from any as government is honoring her with its own gold medal inscribed everlasting olympic champion • the international olympic committee did allow run a con to keep her team gold medal and the silver medal she won in the vote compass*** a spokeswoman says republican senator strom thurmond is doing very well after falling ill saturday • he spent the night it will to read army medical center in washington HLT/NAACL'06 - Practice Talk

9. Story segmentation example • the united states finished at the top with a total of ninety seven medals thirty nine of them gold russian china and australia rounded up its four • andrea run economy seemed to champion style welcome home even though she was stripped of her individual gold medal at the sydney olympics in armenian gymnast tested positive for a banned stimulant • that was in a nonprescription cold medicine she took from any as government is honoring her with its own gold medal inscribed everlasting olympic champion • the international olympic committee did allow run a con to keep her team gold medal and the silver medal she won in the vote compass a spokeswoman says republican senator strom thurmond is doing very well after falling ill saturday • he spent the night it will to read army medical center in washington HLT/NAACL'06 - Practice Talk

10. Lexical Features • TextTiling • Identify boundaries with locally minimal cosine similarity of the preceding and following regions. • LCSeg • Augments the above process by weighting cosine similarity by a lexical chain score: a measure of lexical repetition. • ‘Cue’ Unigrams • Those (stemmed, when available) unigrams that are significantly more likely to appear near the start or end of a story. HLT/NAACL'06 - Practice Talk

11. Acoustic Features • Pitch and Intensity • Min, max, median, mean, std.dev., mean absolute slope • Calculated over previous sentence, and first-order difference between previous and following • Vowel Duration • Mean vowel length, sentence final vowel length, sentence final rhyme length • Voiced/Total 10ms frames as an approximation of speaking rate HLT/NAACL'06 - Practice Talk

12. Speaker-dependent Features • Based on automatic speaker diarization • Performed by our collaborators at U.Washington • Normalization of acoustic features. • Speaker participation features as a rough approximation of speaker “role”. • What percent of the show’s sentences does the speaker of the previous sentence deliver? • First turn? Last turn? • Is this the first speaker in the show? HLT/NAACL'06 - Practice Talk

13. Ruleset Excerpts • (ENG)If (speaker_distribution > .16) and (length > 15.85) and (maxI > 80.5) and (minI < 43.6) and (vowels_per_sec < 3) Then SEGMENT • (MAN)If (speaker_boundary) and (last_speaker_turn) and (speaker_distribution > 0.036) and (vowels_per_sec_norm > 1.03) Then SEGMENT • (ARB)If (following_cue_words > 1) and (preceding_cue_words > 1) and (meanI < 67.8) and (stdev_I > 8.0) Then SEGMENT HLT/NAACL'06 - Practice Talk

14. Results - English HLT/NAACL'06 - Practice Talk

15. Results - Mandarin HLT/NAACL'06 - Practice Talk

16. Results - Arabic HLT/NAACL'06 - Practice Talk

17. Conclusions • The described approach is successful at detecting story boundaries in English and Mandarin BN, though recall could be improved. • The acoustic features shown here and elsewhere to be indicative of topic shifts are not discriminative on Arabic BN. • Arabic has a different intonation strategy • MSA is not any speaker’s native language • Errors from previous processing -- ASR, sentence segmentation -- hinder the effectiveness of acoustic analysis. HLT/NAACL'06 - Practice Talk

18. Thank You {amaxwell,julia}@columbia.edu