---
title: "Sentence Reduction for Automatic Text Summarization (2000)"
description: "Most extractive summarizers copy whole sentences, while professional abstractors usually trim them. 12 references, cited by 68 papers here."
authors: ["Hongyan Jing"]
year: 2000
venue: "Sixth Applied Natural Language Processing Conference"
source: "https://aclanthology.org/A00-1043/"
doi: "10.3115/974147.974190"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/jing-2000-sentence-reduction-automatic-text-summarization/"
---
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# Sentence Reduction for Automatic Text Summarization

**Authors:** [Hongyan Jing](https://md.research.elamur.ai/free/en/authors/hongyan-jing/) · **Year:** 2000 · **Venue:** [Sixth Applied Natural Language Processing Conference](https://md.research.elamur.ai/free/en/venues/anlp/) ([ANLP 2000](https://md.research.elamur.ai/free/en/venues/anlp/2000/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/A00-1043/) · [PDF](https://aclanthology.org/A00-1043.pdf) · [DOI 10.3115/974147.974190](https://doi.org/10.3115/974147.974190) · [OpenAlex](https://openalex.org/W1964326564)

**Citations:** 279 citations (OpenAlex, 2026-10-14) · [68](https://md.research.elamur.ai/free/en/papers/jing-2000-sentence-reduction-automatic-text-summarization/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Most extractive summarizers copy whole sentences, while professional abstractors usually trim them. This paper addresses sentence reduction: deciding which parts of an extracted sentence can be dropped without losing its main point. These parts can be words, prepositional phrases, gerunds, to-infinitives or clauses. The system takes the extracted sentence and its source document, parses the sentence with the IBM ESG parser, and annotates each parse-tree node with three kinds of evidence:
- **Grammatical necessity**, from thematic-role rules and a combined lexicon of verb subcategorizations.
- **Local-context relevance**, from weighted counts of lexical links such as repetition, morphology and WordNet relations.
- **Corpus probabilities** that a subtree is removed, partially reduced or left intact.

A final top-down pass combines these signals into a decision for each subtree. The training and test data are 500 sentences paired with professional reductions, mined automatically from Benton Foundation news abstracts. Humans shortened these sentences by 44.2% on average. The system's decisions agreed with the human ones 81.3% of the time, which suggests reduction can make extractive summaries much more concise and coherent.

## Key points
- The paper builds on an analysis of human summaries that found six editing operations. Reduction is one of them, alongside combination, syntactic transformation, paraphrasing, generalization/specification and reordering.
- The pipeline has five steps: ESG parsing, grammar checking, context scoring, corpus evidence and a final decision pass.
- The lexicon merges COMLEX, Levin's English Verb Classes and Alternations, WordNet, and the WordNet-sense-tagged Brown Corpus. It covers subcategorization frames for over 5,000 verbs and is used to protect obligatory verb arguments.
- The corpus was built with the authors' decomposition program, which aligns phrases in human abstracts with the source articles. The articles are telecommunication news from the Benton Foundation's "Communications-related headlines" service.
- On average, humans shortened the 500 sentences by 44.2%, and the system matched 81.3% of human reduction decisions.

## Interesting ideas
- **Harvesting training pairs from existing summaries:** aligning human abstracts back to their sources yields original/reduced sentence pairs with no dedicated annotation effort. A similar alignment trick could build compression data for Ukrainian from news digests.
- **Obligatoriness relative to the parent node:** each node is marked as required or optional only with respect to its parent, so the tree can be pruned locally and safely. This maps naturally onto dependency trees such as UD.
- **Head-conditioned deletion probabilities with three outcomes:** the model estimates how likely a subtree is to be removed, partly reduced or kept, given the head word. This captures lexical habits, such as editors almost never touching the content clause after reporting verbs, which purely syntactic rules would miss.
- **Typed, weighted lexical links as a topicality signal:** exact repetition counts more than a weaker WordNet relation like hypernymy, and phrase scores are summed up the tree. This is a cheap, interpretable salience measure that could feed other selection or ranking tasks.
- **Evaluating individual decisions rather than whole outputs:** measuring agreement on each keep/drop choice against human edits gives a fine-grained score. That score is easier to diagnose than judging overall summary quality.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Columbia University](https://md.research.elamur.ai/free/en/universities/columbia-university/) — University, New York, United States · 149 works · 48,852 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 9,086 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 8,889 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 9,296 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 9,206 works

🧭 Topics (1): [📝 Summarization](https://md.research.elamur.ai/free/en/topics/summarization/) · 159 works

## Cite

Cite as: Hongyan Jing. 2000. Sentence Reduction for Automatic Text Summarization. Sixth Applied Natural Language Processing Conference.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{jing2000sentence,
  title = {{Sentence Reduction for Automatic Text Summarization}},
  author = {Hongyan Jing},
  booktitle = {Sixth Applied Natural Language Processing Conference},
  year = {2000},
  doi = {10.3115/974147.974190},
  url = {https://doi.org/10.3115/974147.974190}
}
```

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## References

12 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. John Carroll, Guido Minnen, Yvonne Canning, Siobhan Devlin, and John Tait. 1998. Practical simplification of English newspaper text to assist aphasic readers. In Proceedings of AAAI-98 Workshop on Integrating Artificial Intelligence and Assistive Technology, Madison, Wisconsin, July. [find](https://scholar.google.com/scholar?q=John+Carroll%2C+Guido+Minnen%2C+Yvonne+Canning%2C+Siobhan+Devlin%2C+and+John+Tait%2E+1998%2E+Practical+simplification+of+English+newspaper+text+to+assist+aphasic+readers%2E+In+Proceedings+of+AAAI%2D98+Workshop+on+Int)
2. R. Chandrasekar, C. Doran, and B. Srinivas. 1996. Motivations and methods for text simplification. In Proceedings of the 16th International Conference on Computational Linguistics (COLING'96), Copenhagen, Denmark, August. [our card](https://md.research.elamur.ai/free/en/papers/chandrasekar-1996-motivations-methods-text-simplification/)
3. Simon H. Corston-Oliver and William B. Dolan. 1999. Less is more: Eliminating index terms from subordinate clauses. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics(ACL'99), pages 349-356, University of Maryland, Maryland, June. [find](https://scholar.google.com/scholar?q=Simon+H%2E+Corston%2DOliver+and+William+B%2E+Dolan%2E+1999%2E+Less+is+more%3A+Eliminating+index+terms+from+subordinate+clauses%2E+In+Proceedings+of+the+37th+Annual+Meeting+of+the+Association+for+Computational+Lingu)
4. Gregory Grefenstette. 1998. Producing intelligent telegraphic text reduction to provide an audio scanning service for the blind. In Working Notes of A A A I 1998 Spring Symposium on Intelligent Text Summarization, Stanford University, Standford, California, March. [find](https://scholar.google.com/scholar?q=Gregory+Grefenstette%2E+1998%2E+Producing+intelligent+telegraphic+text+reduction+to+provide+an+audio+scanning+service+for+the+blind%2E+In+Working+Notes+of+A+A+A+I+1998+Spring+Symposium+on+Intelligent+Text+S)
5. Hongyan Jing and Kathleen R. McKeown. 1998. Combining multiple, large-scale resources in a reusable lexicon for natural language generation. In Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and the 17th International Conference on Computational Linguistics,… [find](https://scholar.google.com/scholar?q=Hongyan+Jing+and+Kathleen+R%2E+McKeown%2E+1998%2E+Combining+multiple%2C+large%2Dscale+resources+in+a+reusable+lexicon+for+natural+language+generation%2E+In+Proceedings+of+the+36th+Annual+Meeting+of+the+Associatio)
6. Hongyan Jing and Kathleen R. McKeown. 1999. The decomposition of human-written summary sentences. In Proceedings of the 22nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 129-136, University of Berkeley, CA, August. [find](https://scholar.google.com/scholar?q=Hongyan+Jing+and+Kathleen+R%2E+McKeown%2E+1999%2E+The+decomposition+of+human%2Dwritten+summary+sentences%2E+In+Proceedings+of+the+22nd+International+ACM+SIGIR+Conference+on+Research+and+Development+in+Informati)
7. Hongyan Jing and Kathleen R. McKeown. 2000. Cut and paste based text summarization. In Proceedings of NAACL 2000. [find](https://scholar.google.com/scholar?q=Hongyan+Jing+and+Kathleen+R%2E+McKeown%2E+2000%2E+Cut+and+paste+based+text+summarization%2E+In+Proceedings+of+NAACL+2000%2E)
8. Beth Levin. 1993. English Verb Classes and Alternations: A Preliminary Investigation. University of Chicago Press, Chicago, Illinois. [find](https://scholar.google.com/scholar?q=Beth+Levin%2E+1993%2E+English+Verb+Classes+and+Alternations%3A+A+Preliminary+Investigation%2E+University+of+Chicago+Press%2C+Chicago%2C+Illinois%2E)
9. Catherine Macleod and Ralph Grishman, 1995. COMLEX Syntax Reference Manual. Proteus Project, New York University. [find](https://scholar.google.com/scholar?q=Catherine+Macleod+and+Ralph+Grishman%2C+1995%2E+COMLEX+Syntax+Reference+Manual%2E+Proteus+Project%2C+New+York+University%2E)
10. Michael McCord, 1990. English Slot Grammar. IBM. [find](https://scholar.google.com/scholar?q=Michael+McCord%2C+1990%2E+English+Slot+Grammar%2E+IBM%2E)
11. George A. Miller, Richard Beckwith, Christiane Fellbaum, Derek Gross, and Katherine J. Miller. 1990. Introduction to WordNet: An on-line lexical database. International Journal of Lexicography (special issue), 3(4):235-312. [find](https://scholar.google.com/scholar?q=George+A%2E+Miller%2C+Richard+Beckwith%2C+Christiane+Fellbaum%2C+Derek+Gross%2C+and+Katherine+J%2E+Miller%2E+1990%2E+Introduction+to+WordNet%3A+An+on%2Dline+lexical+database%2E+International+Journal+of+Lexicography+%28specia)
12. George A. Miller, Claudia Leacock, Randee Tengi, and Ross T. Bunker. 1993. A semantic concordance. Cognitive Science Laboratory, Princeton University. [our card](https://md.research.elamur.ai/free/en/papers/miller-1993-semantic-concordance/)

*Updated 24 October 2026*