---
title: "SECTOR — paper by Arnold et al. (2019) — summary and ideas"
description: "They propose SECTOR, an end-to-end neural model built on a bidirectional LSTM. 71.6% F1. 55 references, cited by 39 papers here."
authors: ["Sebastian Arnold", "Rudolf Schneider", "Philippe Cudré-Mauroux", "Felix A. Gers", "Alexander Löser"]
year: 2019
venue: "Transactions of the Association for Computational Linguistics"
source: "https://aclanthology.org/Q19-1011/"
doi: "10.1162/tacl_a_00261"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/arnold-2019-sector-neural-model-coherent-topic/"
---
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# SECTOR: A Neural Model for Coherent Topic Segmentation and Classification

**Authors:** Sebastian Arnold, Rudolf Schneider, Philippe Cudré-Mauroux, [Felix A. Gers](https://md.research.elamur.ai/free/en/authors/felix-alexander-gers/) and [Alexander Löser](https://md.research.elamur.ai/free/en/authors/alexander-loser/) · **Year:** 2019 · **Venue:** [Transactions of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/tacl/) ([Transactions of the Association for Computational Linguistics (TACL) 2019](https://md.research.elamur.ai/free/en/venues/tacl/2019/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/Q19-1011/) · [PDF](https://aclanthology.org/Q19-1011.pdf) · [DOI 10.1162/tacl\_a\_00261](https://doi.org/10.1162/tacl_a_00261) · [OpenAlex](https://openalex.org/W2914694065)

**Citations:** 85 citations (OpenAlex, 2026-10-14) · [39](https://md.research.elamur.ai/free/en/papers/arnold-2019-sector-neural-model-coherent-topic/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Long documents often lack explicit headings, or their structure follows domain conventions. This makes it hard for reading, retrieval and QA systems to find which passage covers which topic. The authors treat splitting a text into coherent sections and labeling each section's topic as one joint problem rather than two separate steps.

They propose SECTOR, an end-to-end neural model built on a bidirectional LSTM. It reads plain text sentence by sentence and learns a latent topic embedding, supervised by Wikipedia section headings. Topic labels are predicted per sentence. Section boundaries come from shifts (deviations) between the forward and backward embeddings, so segmentation needs no separate training.

They also release WikiSection, a corpus of English and German Wikipedia articles about diseases and cities with normalized topic labels. Across 20 compared architectures, SECTOR with Bloom filter word encoding and bidirectional segmentation performed best. It clearly outperformed CNN classifiers paired with naive segmentation. This makes it a promising building block for passage-level retrieval in unlabeled corpora such as medical or technical texts.

## Key points
- WikiSection contains 38k articles with 242k labeled sections, keeping both original headings and normalized labels for up to 30 topics. It forms four subsets (English/German × diseases/cities) and is released under CC BY-SA 3.0.
- The best result was 71.6% F1 for joint segmentation and classification of 30 topics on the English cities subset. This is 29.5 F1 points above state-of-the-art CNN classifiers with baseline segmentation.
- In a multi-label variant using about 2.8k raw heading words as topics, SECTOR reached 71.1% MAP.
- On other existing segmentation data sets, the bidirectional LSTM segmentation performed comparably to specialized state-of-the-art segmenters.
- Baselines included LDA, paragraph vectors, CNNs and RNNs. The source code is available under Apache License 2.0 on GitHub (sebastianarnold/SECTOR).

*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

- [Berliner Hochschule für Technik](https://md.research.elamur.ai/free/en/universities/berliner-hochschule-fur-technik/) — University, Berlin, Germany · 7 works · 799 citations
- [University of Fribourg](https://md.research.elamur.ai/free/en/universities/university-of-fribourg/) — University, Fribourg, Switzerland · 4 works · 200 citations

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## Cite

Cite as: Sebastian Arnold, Rudolf Schneider, Philippe Cudré-Mauroux, Felix A. Gers and Alexander Löser. 2019. SECTOR: A Neural Model for Coherent Topic Segmentation and Classification. Transactions of the Association for Computational Linguistics.

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

```bibtex
@article{arnold2019sector,
  title = {{SECTOR: A Neural Model for Coherent Topic Segmentation and Classification}},
  author = {Sebastian Arnold, Rudolf Schneider, Philippe Cudré-Mauroux, Felix A. Gers and Alexander Löser},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2019},
  doi = {10.1162/tacl\_a\_00261},
  url = {https://doi.org/10.1162/tacl_a_00261}
}
```

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*Updated 30 October 2026*