A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Prosody-based automatic segmentation of speech into sentences and topics
Speech Communication - Special issue on accessing information in spoken audio
Generic text summarization using relevance measure and latent semantic analysis
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Advances in Automatic Text Summarization
Advances in Automatic Text Summarization
Automatic summarization of open-domain multiparty dialogues in diverse genres
Computational Linguistics - Summarization
TextTiling: segmenting text into multi-paragraph subtopic passages
Computational Linguistics
Minimizing word error rate in textual summaries of spoken language
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Recent advances in automatic speech summarization
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
Text summarization and singular value decomposition
ADVIS'04 Proceedings of the Third international conference on Advances in Information Systems
Mixed-source multi-document speech-to-text summarization
MMIES '08 Proceedings of the Workshop on Multi-source Multilingual Information Extraction and Summarization
Recent advances in automatic speech summarization
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
Revisiting centrality-as-relevance: support sets and similarity as geometric proximity
Journal of Artificial Intelligence Research
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Speech summarization technology, which extracts important information and removes irrelevant information from speech, is expected to play an important role in building speech archives and improving the efficiency of spoken document retrieval. However, speech summarization has a number of significant challenges that distinguish it from general text summarization. Fundamental problems with speech summarization include speech recognition errors, disfluencies, and difficulties of sentence segmentation. Typical speech summarization systems consist of speech recognition, sentence segmentation, sentence extraction, and sentence compaction components. Most research up to now has focused on sentence extraction, using LSA (Latent Semantic Analysis), MMR (Maximal Marginal Relevance), or feature-based approaches, among which no decisive method has yet been found. Proper sentence segmentation is also essential to achieve good summarization performance. How to objectively evaluate speech summarization results is also an important issue. Several measures, including families of SumACCY and ROUGE measures, have been proposed, and correlation analyses between subjective and objective evaluation scores have been performed. Although these measures are useful for ranking various summarization methods, they do not correlate well with human evaluations, especially when spontaneous speech is targeted.