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A trainable document summarizer
Open accessAbstract
To summarize is to reduce in complexity, and hence in length, while retaining some of the essential qualities of the original.This paper focusses on document extracts, a particular kind of computed document summary.Document extracts consisting of roughly 20% of the original cart be as informative as the full text of a document, which suggests that even shorter extracts may be useful indicative summmies.The trends in our results are in agreement with those of Ed- mundson who used a subjectively weighted combination of features as opposed to training the feature weights using a corpus.We have developed a trainable summarization program that is grounded in a sound statistical framework.
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Cited by
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Hu, M., & Liu, B. (2004) · Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1014052.1014073 · Cited by 7,793
Get To The Point: Summarization with Pointer-Generator Networks
See, A., Liu, P. J., & Manning, C. D. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1099 · Cited by 3,775
The use of MMR, diversity-based reranking for reordering documents and producing summaries
Carbonell, J., & Goldstein, J. (1998) · Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/290941.291025 · Cited by 2,329
A Survey of the Usages of Deep Learning for Natural Language Processing
Otter, D. W., Medina, J. R., & Kalita, J. K. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2979670 · Cited by 1,836
A Survey of Text Summarization Extractive Techniques
Gupta, V., & Lehal, G. S. (2010) · Journal of Emerging Technologies in Web Intelligence · doi:10.4304/jetwi.2.3.258-268 · Cited by 616
Metadata
- Authors
- Kupiec, J., Pedersen, J., & Chen, F.
- Venue
- Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '95
- Publisher
- ACM Press
- Published
- 1995
- Pages
- 68-73
- OpenAlex
- W2101390659
- Cited by
- 1,330
- References
- 0
- FWCI
- 20.42
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- CoChat. (2026). Citation check: A trainable document summarizer. https://app.cochat.ai/papers/10.1145/215206.215333
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- Kupiec, J., Pedersen, J., & Chen, F. (1995). A trainable document summarizer. Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '95. https://doi.org/10.1145/215206.215333
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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.
