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Supervised Spoken Documents Summarization Jointly Considering Utterance Importance and Redundancy by Structured SVM. INTERSPEECH 2012 Hung-Yi Lee, Yu-Yu Chou, Yow-Bang Wang, Lin- S han Lee. Speaker: Hsiao- Tsung Hung. Outline. Introduction Proposed Approach Features for Utterances
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Supervised Spoken Documents Summarization Jointly Considering Utterance Importance and Redundancy by Structured SVM INTERSPEECH 2012 Hung-Yi Lee, Yu-Yu Chou, Yow-Bang Wang, Lin-Shan Lee Speaker: Hsiao-Tsung Hung
Outline • Introduction • Proposed Approach • Features for Utterances • Experiments
Introduction • In extractive spoken document summarization, it is not easy to balance the factors of importance and redundancy.
Proposed Approach • Objective function : feature vector (length, position, semantic topic probabilities, prosodic features) importance redundancy Length constraint
Proposed Approach • The goal of jointly learning and is accomplished by solving the optimization problem below using Structured SVM • : loss function
Features for Utterances 1. Sematic Features– Latent topic entropy • Lower latent topic entropy reveals that thee content of the utterance x is more concentrated on a small number of topics. PLSA
Features for Utterances 2. Similarity with the whole documents • Sim(x,x’) : cosine similarity • Prosodic features • Pause duration, pitch, syllable duration, energy… • Others • Utterance length • Normalized utterance position • The average of the IDF scores for all the terms in transcription