The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Finding topic words for hierarchical summarization
Abstract
Hierarchies have long been used for organization, summarization, and access to information. In this paper we define summarization in terms of a probabilistic language model and use the definition to explore a new technique for automatically generating topic hierarchies by applying a graph-theoretic algorithm, which is an approximation of the Dominating Set Problem. The algorithm efficiently chooses terms according to a language model. We compare the new technique to previous methods proposed for constructing topic hierarchies including subsumption and lexical hierarchies, as well as the top TF.IDF terms. Our results show that the new technique consistently performs as well as or better than these other techniques. They also show the usefulness of hierarchies compared with a list of terms.
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Citations
Cites
The sixth text Retrieval conference (TREC-6)
(1998) · doi:10.6028/nist.sp.500-240 · Cited by 113
Cited by
Learning to cluster web search results
Zeng, H.-J., He, Q.-C., Chen, Z., Ma, W.-Y., & Ma, J. (2004) · Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1008992.1009030 · Cited by 587
Metadata
- Authors
- Lawrie, D., Croft, W. B., & Rosenberg, A.
- Venue
- Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2001-09
- Pages
- 349-357
- OpenAlex
- W1982078690
- Cited by
- 171
- References
- 1
- FWCI
- 6.48
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Finding topic words for hierarchical summarization. https://app.cochat.ai/papers/10.1145/383952.384022
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- Lawrie, D., Croft, W. B., & Rosenberg, A. (2001). Finding topic words for hierarchical summarization. Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval. https://doi.org/10.1145/383952.384022
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
