---
title: "Advances in domain independent linear text — Choi 2000"
description: "The paper tackles linear text segmentation: splitting a written document into consecutive, topically coherent blocks without relying on domain-specific…"
authors: ["Freddy Y. Y. Choi"]
year: 2000
venue: "1st Meeting of the North American Chapter of the Association for Computational Linguistics"
source: "https://aclanthology.org/A00-2004/"
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/choi-2000-advances-domain-independent-linear-text/"
---
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# Advances in domain independent linear text segmentation

**Authors:** Freddy Y. Y. Choi · **Year:** 2000 · **Venue:** 1st Meeting of the North American Chapter of the 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/A00-2004/) · [PDF](https://aclanthology.org/A00-2004.pdf) · [OpenAlex](https://openalex.org/W1626945812)

**Citations:** 569 citations (openalex-snapshot · 2026-10-14) · [125](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper tackles linear text segmentation: splitting a written document into consecutive, topically coherent blocks without relying on domain-specific cues. Choi builds on Reynar's divisive-clustering approach and introduces C99. Each sentence becomes a vector of stemmed, non-stopword token counts. All sentence pairs are compared with the cosine measure, giving a similarity matrix. The author argues that raw cosine values between short sentences are statistically unreliable and vary across a document, so each matrix cell is replaced by its rank among nearby cells. Divisive clustering then repeatedly splits the text where the "inside density" of the diagonal blocks is highest. It stops automatically when the density gain drops unusually sharply. The method was tested on an artificial corpus built from Brown corpus excerpts and scored with Beeferman's error probability. C99 roughly halved the error of Reynar's R98 and ran about seven times faster. It also beat sliding-window (H94) and lexical-chain (K98) segmenters. The work matters because it shows that relying only on the order of similarities makes lexical cohesion far more robust.

## Key points
- Preprocessing removes punctuation and stopwords and applies Porter stemming. Sentence similarity is the cosine of word-stem frequency vectors.
- Each similarity value is replaced by the share of neighbours, within an 11 × 11 mask, that have a lower value. Masks larger than 3 × 3 brought no meaningful accuracy gain. A 1 × 1 mask makes every cell zero and fails completely.
- The test corpus has 700 samples. Each sample joins ten segments, and every segment consists of the opening n sentences taken from a randomly chosen Brown corpus document (news and informative text only). Sample sets cover n ranges of 3–11, 3–5, 6–8 and 9–11.
- On the 3–11 set, the error rate was about 12% for C99(b) (given the segment count), 13% for C99 (automatic stopping) and 22% for R98. Runtime was about 4 s for C99(b) versus about 30 s for R98. Significance was checked with both a t-test and a KS-test.
- Five degenerate baselines anchor the comparison:
  - no boundaries;
  - all boundaries;
  - regular intervals;
  - random boundaries of any number;
  - random boundaries of a fixed count.

## Interesting ideas
- **Rank instead of raw scores.** When individual similarity scores are noisy, keep only their local ordering. Borrowed from non-parametric statistics and image processing, this trick could help any method that works on sparse pairwise similarities, such as short-text clustering or alignment.
- **Segmentation as image processing.** Treating the sentence-by-sentence similarity matrix as an image lets you use image tools directly. Here a rank filter boosts contrast, so the coherent square blocks along the diagonal stand out.
- **Automatic choice of segment count.** The splitting step watches how the density gain changes. After light smoothing, it stops where the drop exceeds the mean plus 1.2 standard deviations. This gives a cheap, reusable stopping rule for hierarchical splitting or merging.
- **Precomputing block sums.** Sums over all diagonal blocks are computed once, working outward from the main diagonal. This simple dynamic-programming idea removes the main cost of each clustering step.
- **Synthetic evaluation with known boundaries.** Gluing together excerpts from different documents produces exact gold boundaries at no annotation cost. Pairing this with trivial baselines makes the reported error rates easy to interpret, and the approach transfers easily to corpora in other languages, including Ukrainian.

*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: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

125 papers of our library cite it (of the 140884 whose bibliographies we read); 5 of them have a page here, 120 not yet.

- [Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization](https://md.research.elamur.ai/free/en/papers/chen-2020-multi-view-sequence-sequence-models/)
- [Discourse Segmentation of Multi-Party Conversation](https://md.research.elamur.ai/free/en/papers/galley-2003-discourse-segmentation-multi-party-conversation/)
- [Language Classification and Segmentation of Noisy Documents in Hebrew Scripts](https://md.research.elamur.ai/free/en/papers/lingbulk-2012-zhicharevich-language-classification-segmentation-noisy-documents/)
- [A Critique and Improvement of an Evaluation Metric for Text Segmentation](https://md.research.elamur.ai/free/en/papers/pevzner-2002-critique-improvement-evaluation-metric-text/)
- [Summarizing Scientific Articles: Experiments with Relevance and Rhetorical Status](https://md.research.elamur.ai/free/en/papers/teufel-2002-summarizing-scientific-articles-experiments-relevance/)

## Cite

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

```bibtex
@inproceedings{choi2000advances,
  title = {{Advances in domain independent linear text segmentation}},
  author = {Choi, Freddy Y. Y.},
  booktitle = {1st Meeting of the North American Chapter of the Association for Computational Linguistics},
  year = {2000},
  url = {https://aclanthology.org/A00-2004/}
}
```

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## References

38 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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