Newscast Speech Summarization via Sentencê Shortening based on Prosodic Features ● Explosion of information ● No time to process everything Thus, automatic summarization is required. Not only text, but also speech
Speech Summarization
Our approach: direct speech summarizer based on prosodic features Conventional one: speech recognizer + text summarizer Why? Speech conveys more information than its transcription. Target: newscast speech Extracting pitch pattern By a method proposed by Hirose et al. Detecting summary units (1/2) The base-pitch is a representative value of an accent phrase. Newscast Speech Summarization Five Steps: 1. Extract pitch pattern 2. Extract accent phrases 3. Detect summary units 4. Analyze dependency structures 5. Select important summary units Extracting accent phrases The beginning of each accent phrase is determined by picking up the rise in F0.
Detecting summary units (2/2)
Heuristics: adjust the boundaries to cor- rect mis-detection caused by exceptional pitch rises for emphasis or paraphrasing. Approximate analysis
Estimating parameters
By trial and error ● for accent phrase detection ● for summary units selection with NHK news speech database.
Speech summarization ratio
playback time of summary SS-ratio = × 100(%)
Method 2 produced shorter summaries.
Evaluating phonological naturalness

Method 2 also showed the best and sta- ble performance.
Selecting summary units
Three methods: method 1: based on common heuristics for news texts method 2: based on heuristics of prosodic features with absolute parameters for each speaker method 3: based on heuristics of prosodic features with relative parameters
Evaluate summary units detection
Ten articles, 52 sentences ● Prepare correct summary unit boundaries by hand
Preservation ratio of important information Questionnaire with eight subjects Scale: 1 to 5 (best: 5, worst: 1)
Method 2 showed the best and stable per- formance. Comparing with conventional method (1/2) Comparing with conventional method (2/2) Standard: six subjects select important parts (S-ratio (summarization ratio): 56.8%)
Mikami’s produced shorter summaries.
Discussions (2/2) - comparison

。 Mikami’s failed summary unit detection → longer parts were eliminated  Ours seems robust Quality evaluation is needed. Thank you!
Appendix B
Example of detected summary unit " || " indicates a boundary.て全国銀行協会連合会のまとめによりますと||おととし以降事故が|| 企業や個人に貸し出しが資金の総額がで先月九月末現在で‖二百九十五勝千三億円で||去年の同じ月に比べて寄点三パーセント増えました
Discussions (1/2) - speech summarization
。 Fine summary unit detection whereas troublesome heuristics and parameter setting ● Method 2 is the best strategy thanks to the parameter settings
Conclusion
Feasibility of speech summarization is shown Open problems: ● determine parameters dynamically ● consider other prosodic features
Appendix A
Example of extracted accent phrase " | " indicates a boundary.て｜全国銀行協会連合会のまとめによりますと|おととし以降事故が|企業や個人に|貸し出しが|資金の総額がで先月九月末現在で|二百九十|五勝|千三百一億円で|去年の同じ月に比べて|零点三バーセント増えました
Appendix C
Underlines indicate eliminated parts.て全国銀行協会連合会のまとめによりますと ||おととし以降事故が || 企業や個人に貸し出しが資金の総額がで先月九月末現在で||二百九十五勝千三百一億円で||去年の同じ月に比べて零点三パーセント増えました
Appendix D
Mannualy transcribed:え全国銀行協会連合会のまとめによりま
すと、え都市銀行十行が、企業や個人に貸し出した資金の総額は、え先月ーう末
現在でで、二百う一十五兆千三百一億円で、
え去年の同じ月に比べて〇・三％増えました。 Hermens Ipaech Surrasinateer de Berterea  Densering beauti se Proceeds Procees - P 26.2K