---
title: "Information Status Distinctions and Referring Expressions: An"
description: "The paper addresses how a multi-document news summarizer should word its mentions of people. 71 references, cited by 24 papers here."
authors: ["Advaith Siddharthan", "Ani Nenkova", "Kathleen McKeown"]
year: 2011
venue: "Computational Linguistics"
source: "https://aclanthology.org/J11-4007/"
doi: "10.1162/coli_a_00077"
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/siddharthan-2011-information-status-distinctions-referring-expressions/"
---
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# Information Status Distinctions and Referring Expressions: An Empirical Study of References to People in News Summaries

**Authors:** [Advaith Siddharthan](https://md.research.elamur.ai/free/en/authors/advaith-siddharthan/), [Ani Nenkova](https://md.research.elamur.ai/free/en/authors/ani-nenkova/) and [Kathleen McKeown](https://md.research.elamur.ai/free/en/authors/kathleen-mckeown/) · **Year:** 2011 · **Venue:** [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/) ([Computational Linguistics 2011](https://md.research.elamur.ai/free/en/venues/cl/2011/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **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/J11-4007/) · [PDF](https://aclanthology.org/J11-4007.pdf) · [DOI 10.1162/coli\_a\_00077](https://doi.org/10.1162/coli_a_00077) · [OpenAlex](https://openalex.org/W2144719390)

**Citations:** 46 citations (OpenAlex, 2026-10-14) · [24](https://md.research.elamur.ai/free/en/papers/siddharthan-2011-information-status-distinctions-referring-expressions/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper addresses how a multi-document news summarizer should word its mentions of people. Sentences pulled from different source articles often describe a person with too little detail, too much, or the same detail twice. The authors separate three kinds of information status: whether a mention is the first one in the text (discourse-new vs. discourse-old), whether the reader probably already knows the person (hearer-new vs. hearer-old), and whether the person is central or peripheral to the story (major vs. minor). They combine theory with a corpus study of human-written summaries. They then train statistical models that predict familiarity and salience from the summarizer's input articles alone, without manual annotation. The features come from the lexical and syntactic form of the references and how often they occur. These predictions guide rewriting choices: whether to name a person, whether to add a role or affiliation, and whether modifiers go before or after the name. In a reader evaluation, the rewritten extractive summaries were judged more coherent and were generally preferred. This shows that cheaply inferred facts about entities can guide reference generation better than distractor-elimination frameworks.

## Key points
- The paper integrates two earlier studies: Nenkova and McKeown (2003) on first vs. subsequent mentions, and Nenkova, Siddharthan, and McKeown (2005) on familiarity and global salience.
- In news, descriptive material about a person almost always appears at the first mention. Coreference chains in summaries are short, so the work focuses on initial references and does not model pronouns or common-noun coreference.
- Rewriting all entities is risky: in Steinberger et al. (2007), about one third of substitutions were wrong. Limiting edits to people produces fewer changes, and readers judge the result better.
- Generation covers content selection (name, role, affiliation) and realization (pre- vs. post-modification of the name).
- Readers were very sensitive to the choice of role. There were 17 complaints about a missing specific role versus 3 about a missing affiliation. Analysis errors were punished, for example the role "spin doctor" was wrongly attached to David Cameron.

## Interesting ideas
- The way sources mention a person (bare name vs. name with description) and how often they do so are evidence of whether readers already know that person. Familiarity is inferred from surface patterns, not stored knowledge.
- Textual newness, reader familiarity, and narrative importance are treated as three separate axes rather than a single givenness scale. This allows independent decisions about naming and describing someone.
- Restricting automatic rewriting to one narrow, high-precision class (people) gives a net quality gain, whereas rewriting every entity introduces many errors.
- The error analysis shows that misreading a possessive appositive (a role belonging to the possessor's associate) moves the role onto the wrong person, and readers punish this heavily.
- The evaluation counts readers' free-text complaints by type, such as missing role vs. missing affiliation. This shows which attribute matters most, beyond a single preference score.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (world): entity records in the world of a text could carry a derived "probably known to the reader" flag. The flag would be computed from mention form and frequency, with the evidence kept for explanation.
- **I2** (back): when generating from the meaning graph, the realizer could choose name, role, and affiliation from these three axes separately. This would make each wording decision traceable.
- **I3** (learn): self-induced repair rules could be scoped to narrow, well-understood categories first. Each rule would be validated against negative controls before its scope is widened.
- **I4** (tree): add regression cases where an appositive role is headed by a possessed noun. This checks that the parser attaches the role to the correct referent.
- **I5** (answer): when grading generated answers or explanations, categorized error tallies could reveal which entity attributes users actually need.

*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

- [University of Pennsylvania](https://md.research.elamur.ai/free/en/universities/university-of-pennsylvania/) — University, Philadelphia, United States · 221 works · 63,234 citations
- [Columbia University](https://md.research.elamur.ai/free/en/universities/columbia-university/) — University, New York, United States · 241 works · 54,053 citations
- [University of Aberdeen](https://md.research.elamur.ai/free/en/universities/university-of-aberdeen/) — University, Aberdeen, United Kingdom · 20 works · 1,385 citations

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## Cite

Cite as: Advaith Siddharthan, Ani Nenkova and Kathleen McKeown. 2011. Information Status Distinctions and Referring Expressions: An Empirical Study of References to People in News Summaries. Computational Linguistics.

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

```bibtex
@article{siddharthan2011information,
  title = {{Information Status Distinctions and Referring Expressions: An Empirical Study of References to People in News Summaries}},
  author = {Advaith Siddharthan, Ani Nenkova and Kathleen McKeown},
  journal = {Computational Linguistics},
  year = {2011},
  doi = {10.1162/coli\_a\_00077},
  url = {https://doi.org/10.1162/coli_a_00077}
}
```

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## References

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*Updated 1 November 2026*