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Generic summarization and keyphrase extraction using mutual reinforcement principle and sentence clustering
Abstract
A novel method for simultaneous keyphrase extraction and generic text summarization is proposed by modeling text documents as weighted undirected and weighted bipartite graphs. Spectral graph clustering algorithms are useed for partitioning sentences of the documents into topical groups with sentence link priors being exploited to enhance clustering quality. Within each topical group, saliency scores for keyphrases and sentences are generated based on a mutual reinforcement principle. The keyphrases and sentences are then ranked according to their saliency scores and selected for inclusion in the top keyphrase list and summaries of the document. The idea of building a hierarchy of summaries for documents capturing different levels of granularity is also briefly discussed. Our method is illustrated using several examples from news articles, news broadcast transcripts and web documents.
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Cited by
PositionRank: An Unsupervised Approach to Keyphrase Extraction from Scholarly Documents
Florescu, C., & Caragea, C. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1102 · Cited by 331
A cross-collection mixture model for comparative text mining
Zhai, C., Velivelli, A., & Yu, B. (2004) · Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1014052.1014150 · Cited by 272
Citation-Enhanced Keyphrase Extraction from Research Papers: A Supervised Approach
Caragea, C., Bulgarov, F. A., Godea, A., & Das Gollapalli, S. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1150 · Cited by 134
Metadata
- Authors
- Zha, H.
- Venue
- Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2002-08-11
- Pages
- 113-120
- OpenAlex
- W2034721576
- Cited by
- 252
- References
- 0
- FWCI
- 3.53
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Zha, H. (2002). Generic summarization and keyphrase extraction using mutual reinforcement principle and sentence clustering. Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval. https://doi.org/10.1145/564376.564398
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