The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Graph-based keyword extraction for single-document summarization
Open accessAbstract
In this paper, we introduce and compare between two novel approaches, supervised and unsupervised, for identifying the keywords to be used in extractive summarization of text documents. Both our approaches are based on the graph-based syntactic representation of text and web documents, which enhances the traditional vector-space model by taking into account some structural document features. In the supervised approach, we train classification algorithms on a summarized collection of documents with the purpose of inducing a keyword identification model. In the unsupervised approach, we run the HITS algorithm on document graphs under the assumption that the top-ranked nodes should represent the document keywords. Our experiments on a collection of benchmark summaries show that given a set of summarized training documents, the supervised classification provides the highest keyword identification accuracy, while the highest F-measure is reached with a simple degree-based ranking. In addition, it is sufficient to perform only the first iteration of HITS rather than running it to its convergence.
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Nallapati, R., Zhou, B., dos Santos, C., Gulcehre, C., & Xiang, B. (2016) · Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning · doi:10.18653/v1/k16-1028 · Cited by 2,361
Ensemble of keyword extraction methods and classifiers in text classification
Onan, A., Korukoğlu, S., & Bulut, H. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.03.045 · Cited by 630
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
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
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Metadata
- Authors
- Litvak, M., & Last, M.
- Venue
- Proceedings of the Workshop on Multi-source Multilingual Information Extraction and Summarization - MMIES '08
- Published
- 2008
- Pages
- 17
- OpenAlex
- W2043004216
- Cited by
- 272
- References
- 14
- FWCI
- 5.95
- Type
- conference paper
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
