---
title: "Text Tiling — paper by Hearst (1997) — summary and ideas"
description: "Hearst addresses how to split long expository texts, such as magazine articles or reports without… 77 references, cited by 49 papers here."
authors: ["Marti A. Hearst"]
year: 1997
venue: "Computational Linguistics"
source: "https://aclanthology.org/J97-1003/"
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---
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# Text Tiling: Segmenting Text into Multi-paragraph Subtopic Passages

**Authors:** [Marti A. Hearst](https://md.research.elamur.ai/free/en/authors/marti-a-hearst/) · **Year:** 1997 · **Venue:** [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/) ([Computational Linguistics 1997](https://md.research.elamur.ai/free/en/venues/cl/1997/)) · **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/J97-1003/) · [PDF](https://aclanthology.org/J97-1003.pdf)

**Citations:** [49](https://md.research.elamur.ai/free/en/papers/hearst-1997-text-tiling-segmenting-text-into/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Hearst addresses how to split long expository texts, such as magazine articles or reports without headings, into contiguous, non-overlapping blocks of several paragraphs, each covering one subtopic. Paragraph breaks often serve layout more than content, so a coarser unit is needed. The paper models a text as a series of local subtopic discussions nested under one or more overarching main topics. TextTiling detects shifts between subtopics using only lexical repetition, with no thesaurus, knowledge base, or discourse markers. The algorithm tokenizes the text into terms and pseudo-sentences, scores each gap between units by lexical similarity, and places boundaries at the deepest dips in the resulting score curve. On 12 texts, the automatic boundaries agree reasonably well with human judgments, though there is room for improvement. The work matters because it supplies passage-sized units for information retrieval, summarization, hypertext construction, and on-screen display. It has also been applied in several other languages.

## Key points
- The pipeline has three stages: tokenization, gap scoring, and boundary detection at the largest valleys of the similarity plot.
- Three scoring schemes were designed: blocks, vocabulary introductions, and lexical chains. The article reports evaluation results for blocks and vocabulary introductions only.
- Evaluation compares the output against human-identified subtopic boundaries in 12 texts. A running example is the 21-paragraph science article "Stargazers."
- Adding thesaural relations in earlier versions lowered performance. WordNet-based relatedness is proposed as future work.
- Others used TextTiling for Arabic news without paragraph breaks (Hasnah 1996), Japanese (Nomoto and Nitta 1994), and aligning English/German/French texts (van der Eijk 1994). Karlgren (1996) found relevant documents contain more TextTiles. A C implementation was offered free for research.

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

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## Cite

Cite as: Marti A. Hearst. 1997. Text Tiling: Segmenting Text into Multi-paragraph Subtopic Passages. Computational Linguistics.

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

```bibtex
@article{hearst1997text,
  title = {{Text Tiling: Segmenting Text into Multi-paragraph Subtopic Passages}},
  author = {Marti A. Hearst},
  journal = {Computational Linguistics},
  year = {1997},
  url = {https://aclanthology.org/J97-1003/}
}
```

- [← previous Probabilistic Coreference in Information Extraction](https://md.research.elamur.ai/free/en/papers/kehler-1997-probabilistic-coreference-information-extraction/)
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## References

77 references, 7 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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*Updated 29 October 2026*