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Extractive Summarization of Meeting Recordings

Extractive Summarization of Meeting Recordings. Gabriel Murray, Steve Renals, Jean Carletta Centre for Speech Technology Research University of Edinburgh, Edinburgh EH8 9LW, Scotland Presenter: Yi-Ting Chen. Reference.

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Extractive Summarization of Meeting Recordings

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  1. Extractive Summarization of Meeting Recordings Gabriel Murray, Steve Renals, Jean CarlettaCentre for Speech Technology ResearchUniversity of Edinburgh, Edinburgh EH8 9LW, Scotland Presenter: Yi-Ting Chen

  2. Reference • Gabriel Murray, Steve Renals, Jean Carletta, “Extractive Summarization of Meeting Recordings”, Eurospeech 2005. • Jaime Carbonell, Jade Goldstein, “The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries”, In Proceedings of ACM-SIGIR'98, Melbourne, Australia, August 1998.

  3. Outline • Introduction • Summarization Approaches • Maximal Marginal Relevance (MMR) • Latent Semantic Analysis (LSA) • Feature-based Approaches • Experiments Setup • Results • Conclusion and Future Work

  4. Introduction • Most work in speech summarization has been in the domain of broadcast news • It has been demonstrated that standard extractive text summarization techniques, using classifiers based on textual and structural features, work well on broadcast • Summarizing conversational speech is substantially different from text summarization • In this paper we investigate extractive summarization of multiparty meetings, using the ICSI Meeting Corpus • Experiments were carried out using both human transcriptions and the output of an automatic speech recognizer and the quality of the summaries were evaluated using ROUGE

  5. Summarization Approaches(1/5) • Maximal Marginal Relevance (MMR) • MMR is based on the vector-space model of text retrieval, and is well-suited to query-based and multi-document summarization • In MMR, sentences are chosen according to a weighted combination of their relevance to a query and their redundancy with the sentences that have already been extracted • Both relevance and redundancy are measured using cosine similarity

  6. Summarization Approaches(2/5) • Maximal Marginal Relevance (MMR) • Where D is the average document vector, Summ is the average vector from the set of sentences already selected • is annealed, so that relevance is emphasized when the summary is still short, and as the summary grows longer the emphasis is increasingly put on minimizing redundancy

  7. M sentences Information of word j Information of sentence i J content words Summarization Approaches(3/5) • Latent Semantic Analysis (LSA) • LSA is a vector-space approach which involves projection of the term-document matrix to a reduced dimension representation • The term-document matrix is decomposed as follows: • Steinberger and Jezek have offered two strong criticisms of the Gong and Liu approach • Firstly, the method described above ties the dimensionality reduction to the desired summary length • Second, a sentence may score highly but never “win” in any dimension

  8. Summarization Approaches(4/5) • Latent Semantic Analysis (LSA) • The same concerns were addressed, following the Gong and Liu approach, but rather than extracting the best sentence for each topic, the n best sentences are extracted • The number of sentences in the summary that will come from the first topic is determined by the percentage that the largest singular value represents out of the sum of all singular values, and so on for each topic

  9. Dimension reduction SVD Weighted word-frequency vector Weighted singular-value vector Reduced dimension vector Summarization Approaches(5/5) • Feature-based Approaches • 1. Gaussian mixture models for the extracted and non-extracted classes • The prosodic features were the mean and standard deviation of F0, energy, and duration, all estimated and normalized at the word-level, then averaged over the utterance • The lexical features were both TFIDF-based: the average and the maximum TFIDF score for the utterance • 2. The second feature-based approach created single LSA-based sentence scores

  10. Experiments Setup • Human summaries of the ICSI Meeting corpus were used for evaluation and for training the feature-based approaches • An evaluation set of six meeting was defined and multiple human summaries were created for these meetings • The ROUGE evaluation approach were used

  11. Results(2/2) • All of the machine summaries were 10% of the original document length • Of the four approaches to summarization used herein, the latent semantic analysis method performed the best on every meeting tested for every ROUGE measure with the exception of ROUGE-3 and ROUGE-4

  12. Results(2/2) • Valenza et al and Zechner and Waibel both observed that the WER of extracted summaries was significantly lower than the overall WER in the case of broadcast news

  13. Conclusion and Future Work • Though the LSA method consistently performed the best, it was not a significant improvement over MMR and does not share some of the advantages of MMR • The focus in the immediate future will be put on greatly expanding the prosodic database and on building various types of classifiers for the feature-based approach • Finding a method of automatic utterance detection

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