---
title: "Using Coreference Chains for Text Summarization — Azzam 1999"
description: "The paper tests whether coreference chains can drive generic extractive summarization, on the assumption that a text is largely about one central entity."
authors: ["Saliha Azzam", "Kevin Humphreys", "Robert Gaizauskas"]
year: 1999
venue: "Coreference and Its Applications"
source: "https://aclanthology.org/W99-0211/"
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/azzam-1999-coreference-chains-text-summarization/"
---
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# Using Coreference Chains for Text Summarization

**Authors:** [Saliha Azzam](https://md.research.elamur.ai/free/en/authors/saliha-azzam/), [Kevin Humphreys](https://md.research.elamur.ai/free/en/authors/kevin-humphreys/), [Robert Gaizauskas](https://md.research.elamur.ai/free/en/authors/robert-gaizauskas/) · **Year:** 1999 · **Venue:** Coreference and Its Applications · **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/W99-0211/) · [PDF](https://aclanthology.org/W99-0211.pdf) · [OpenAlex](https://openalex.org/W1978117656)

**Citations:** 71 citations (openalex-snapshot · 2026-10-14) · [13](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper tests whether coreference chains can drive generic extractive summarization, on the assumption that a text is largely about one central entity. The authors add a module to LaSIE, the Sheffield information extraction system built for MUC coreference tasks. The module takes every chain in LaSIE's discourse model and picks one "best" chain using heuristics: how many mentions it has (length), how much of the text it covers (spread), where it starts, and focus information from a focus-based pronoun resolution algorithm. The summary is simply the source sentences that mention the chosen entity, in their original order. Unlike lexical-chain methods (Barzilay and Elhadad), this approach relies on true identity of reference, including pronouns. For evaluation, the authors reuse MUC-6 materials as an automatic substitute for SUMMAC-style manual judgments. They test how well summaries of 30 MUC-6 texts support template filling, a question-answering-like task. Topic-filtered single-chain summaries were very short and gave very low recall. Generic summaries were longer and reached higher recall. The work is a preliminary step toward coreference-based summarization and automated summary evaluation.

## Key points
- **System and pipeline.** The method runs on LaSIE (MUC-6/MUC-7). LaSIE processes text in three stages: lexical preprocessing, two-pass chart parsing with semantic interpretation, and discourse interpretation. Each coreference chain is stored as merged instances with character offsets.
- **Selection criteria.** Chains are chosen by length, spread (byte distance between first and last mention), chain start, and focus-based variants. These criteria can be used alone or combined, and ties are broken with a secondary criterion.
- **Evaluation design.** The authors summarized 30 MUC-6 evaluation texts and measured how much MUC-6 template-filling performance survived. The MUC-6 scenario involves three kinds of entity: an organisation, a managerial position, and the individual who holds it.
- **Topic-linked results.** Summaries restricted to chains linked to the topic description scored roughly 1% recall and 80% precision. They kept about 6.1% of the original word count. The authors attribute the low recall to a single chain rarely covering all three entity types.
- **Generic results.** Coreference-chain summaries scored about 30–33% recall and 65–67% precision. Focus-chain summaries scored about 19% recall and 73% precision, at roughly 18–20% of the original length. Some figures, such as word-count ratios, are garbled in the extracted PDF. The authors caution that these scores mostly reflect overlap between text topics and the query topic.

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

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/)

## Cited by

Cited by

13 papers of our library cite it (of the 140673 whose bibliographies we read); 4 of them have a page here, 9 not yet.

- [Dual Cache for Long Document Neural Coreference Resolution](https://md.research.elamur.ai/free/en/papers/guo-2023-dual-cache-long-document-neural/)
- [Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities](https://md.research.elamur.ai/free/en/papers/hsu-2022-contrastive-representation-learning-cross-document/)
- [A Constraint-Based Hypergraph Partitioning Approach to Coreference Resolution](https://md.research.elamur.ai/free/en/papers/sapena-2013-constraint-based-hypergraph-partitioning-approach/)
- [Understanding Mention Detector-Linker Interaction in Neural Coreference Resolution](https://md.research.elamur.ai/free/en/papers/wu-2021-understanding-mention-detector-linker-interaction/)

## Cite

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

```bibtex
@inproceedings{azzam1999using,
  title = {{Using Coreference Chains for Text Summarization}},
  author = {Azzam, Saliha and Humphreys, Kevin and Gaizauskas, Robert},
  booktitle = {Coreference and Its Applications},
  year = {1999},
  doi = {10.3115/1608810.1608825},
  url = {https://doi.org/10.3115/1608810.1608825}
}
```

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## References

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

1. S. Azzam, K. Humphreys, and R. Gaizauskas. 1998. Evaluating a focus-based approach to anaphora resolution. In Proceedings of COLING-ACL'98, pages 74-78. [find](https://scholar.google.com/scholar?q=S%2E+Azzam%2C+K%2E+Humphreys%2C+and+R%2E+Gaizauskas%2E+1998%2E+Evaluating+a+focus%2Dbased+approach+to+anaphora+resolution%2E+In+Proceedings+of+COLING%2DACL%2798%2C+pages+74%2D78%2E) [our card](https://md.research.elamur.ai/free/en/papers/azzam-1998-evaluating-focus-based-approach-anaphora/)
2. A. Bagga and B. Baldwin. 1998. Entity-based cross-document coreferencing using the vector space model. In Proceedings of the COLING-ACL'98 Joint Conference (the 17th International Conference on Computational Linguistics and 36th Annual Meeting of the Association for Computational Linguisities, Montr… [find](https://scholar.google.com/scholar?q=A%2E+Bagga+and+B%2E+Baldwin%2E+1998%2E+Entity%2Dbased+cross%2Ddocument+coreferencing+using+the+vector+space+model%2E+In+Proceedings+of+the+COLING%2DACL%2798+Joint+Conference+%28the+17th+International+Conference+on+Comput)
3. B. Baldwin and T.S. Morton. 1998. Dynamic coreference-based summarization. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP'98). [find](https://scholar.google.com/scholar?q=B%2E+Baldwin+and+T%2ES%2E+Morton%2E+1998%2E+Dynamic+coreference%2Dbased+summarization%2E+In+Proceedings+of+the+Conference+on+Empirical+Methods+in+Natural+Language+Processing+%28EMNLP%2798%29%2E) [our card](https://md.research.elamur.ai/free/en/papers/baldwin-1998-dynamic-coreference-based-summarization/)
4. R. Barzilay and M. Elhadad. 1997. Using lexical chains for text summarization. In Proceedings of the ACL Workshop on Intelligent Scalable Text Summarization, pages 10-17, Madrid, July. [find](https://scholar.google.com/scholar?q=R%2E+Barzilay+and+M%2E+Elhadad%2E+1997%2E+Using+lexical+chains+for+text+summarization%2E+In+Proceedings+of+the+ACL+Workshop+on+Intelligent+Scalable+Text+Summarization%2C+pages+10%2D17%2C+Madrid%2C+July%2E)
5. DARPA: Defense Advanced Research Projects Agency. 1998. Proceedings of the Seventh Message Understanding Conference (MUC-7). Available at http;//www .muc. s a i c . com. [find](https://scholar.google.com/scholar?q=DARPA%3A+Defense+Advanced+Research+Projects+Agency%2E+1998%2E+Proceedings+of+the+Seventh+Message+Understanding+Conference+%28MUC%2D7%29%2E+Available+at+http%3B%2F%2Fwww+%2Emuc%2E+s+a+i+c+%2E+com%2E)
6. R. Gaizanskas and K. Humphreys. 1997. Quantitative Evaluation of Coreference Algorithms in an Information Extraction System. Technical report CS - 97 - 19, Department of Computer Science, University of Sheffield. [find](https://scholar.google.com/scholar?q=R%2E+Gaizanskas+and+K%2E+Humphreys%2E+1997%2E+Quantitative+Evaluation+of+Coreference+Algorithms+in+an+Information+Extraction+System%2E+Technical+report+CS+%2D+97+%2D+19%2C+Department+of+Computer+Science%2C+University+o)
7. R. Gaizauskas, T. Wakao, K Humphreys, H. Cunningham, and Y. Wilks. 1995. Description of the LaSIE system as used for MUC-6. In Proceedings of the Sixth Message Understanding Conference (MUC-6), pages 207-220. Morgan Kaufmann. [find](https://scholar.google.com/scholar?q=R%2E+Gaizauskas%2C+T%2E+Wakao%2C+K+Humphreys%2C+H%2E+Cunningham%2C+and+Y%2E+Wilks%2E+1995%2E+Description+of+the+LaSIE+system+as+used+for+MUC%2D6%2E+In+Proceedings+of+the+Sixth+Message+Understanding+Conference+%28MUC%2D6%29%2C+pages+)
8. M.A.K. Halliday and R. Hasan. 1976. Cohesion in English. Longman, London. [find](https://scholar.google.com/scholar?q=M%2EA%2EK%2E+Halliday+and+R%2E+Hasan%2E+1976%2E+Cohesion+in+English%2E+Longman%2C+London%2E)
9. I. Mani, D. House, G. Klein, L. Hirschman, L. Obrst, T. Firmin, M. Chrzanowski, and B. Sundheim. 1998. The TIPSTER SUMMAC text summarization evaluation: Final report. MITRE Technical Report MTR 98W0000138, MITRE. [find](https://scholar.google.com/scholar?q=I%2E+Mani%2C+D%2E+House%2C+G%2E+Klein%2C+L%2E+Hirschman%2C+L%2E+Obrst%2C+T%2E+Firmin%2C+M%2E+Chrzanowski%2C+and+B%2E+Sundheim%2E+1998%2E+The+TIPSTER+SUMMAC+text+summarization+evaluation%3A+Final+report%2E+MITRE+Technical+Report+MTR+98W000)
10. J. Morris and G. Hirst. 1994. Lexical cohesion computed by thesanral relations as an indicator of the structure of text. Computational Linguistics, 17(1):21-45. [find](https://scholar.google.com/scholar?q=J%2E+Morris+and+G%2E+Hirst%2E+1994%2E+Lexical+cohesion+computed+by+thesanral+relations+as+an+indicator+of+the+structure+of+text%2E+Computational+Linguistics%2C+17%281%29%3A21%2D45%2E)
11. K. Sparck Jones. 1993. What might be in summary? In Knorz, Krause, and Womser-Hacker, editors, Information Retrieval 93: Von der Mod-ellie~ung zur Anwendung, pages 9-26. Universit-atsverlag Konstanz. [find](https://scholar.google.com/scholar?q=K%2E+Sparck+Jones%2E+1993%2E+What+might+be+in+summary%3F+In+Knorz%2C+Krause%2C+and+Womser%2DHacker%2C+editors%2C+Information+Retrieval+93%3A+Von+der+Mod%2Dellie%7Eung+zur+Anwendung%2C+pages+9%2D26%2E+Universit%2Datsverlag+Konstanz%2E)