# SECTOR: A Neural Model for Coherent Topic Segmentation and Classification

arXiv: https://arxiv.org/abs/1902.04793 · HTML: https://mlsanity.com/paper/1902.04793/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2019.

## Built on by (most cited first, 30 of 48)

- [Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration](https://mlsanity.com/paper/2102.04110/) (2021)
- [Unsupervised Topic Segmentation of Meetings with BERT Embeddings](https://mlsanity.com/paper/2106.12978/) (2021)
- [Improving Unsupervised Dialogue Topic Segmentation with Utterance-Pair Coherence Scoring](https://mlsanity.com/paper/2106.06719/) (2021)
- [Toward Unifying Text Segmentation and Long Document Summarization](https://mlsanity.com/paper/2210.16422/) (2022)
- [TIAGE: A Benchmark for Topic-Shift Aware Dialog Modeling](https://mlsanity.com/paper/2109.04562/) (2021)
- [Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence](https://mlsanity.com/paper/2110.07160/) (2021)
- [Language modeling via stochastic processes](https://mlsanity.com/paper/2203.11370/) (2022)
- [Improving Long Document Topic Segmentation Models With Enhanced Coherence Modeling](https://mlsanity.com/paper/2310.11772/) (2023)
- [SuperDialseg: A Large-scale Dataset for Supervised Dialogue Segmentation](https://mlsanity.com/paper/2305.08371/) (2023)
- [PODTILE: Facilitating Podcast Episode Browsing with Auto-generated Chapters](https://mlsanity.com/paper/2410.16148/) (2024)
- [Enhancing Retrieval Augmented Generation with Hierarchical Text Segmentation Chunking](https://mlsanity.com/paper/2507.09935/) (2025)
- [Human Guided Exploitation of Interpretable Attention Patterns in Summarization and Topic Segmentation](https://mlsanity.com/paper/2112.05364/) (2021)
- [Structured Summarization: Unified Text Segmentation and Segment Labeling as a Generation Task](https://mlsanity.com/paper/2209.13759/) (2022)
- [Multi-Granularity Prompts for Topic Shift Detection in Dialogue](https://mlsanity.com/paper/2305.14006/) (2023)
- [Recent Trends in Linear Text Segmentation: a Survey](https://mlsanity.com/paper/2411.16613/) (2024)
- [Membox: Weaving Topic Continuity into Long-Range Memory for LLM Agents](https://mlsanity.com/paper/2601.03785/) (2026)
- [Predicting Above-Sentence Discourse Structure using Distant Supervision from Topic Segmentation](https://mlsanity.com/paper/2112.06196/) (2021)
- [Hierarchical Interpretation of Neural Text Classification](https://mlsanity.com/paper/2202.09792/) (2022)
- [Improving Topic Segmentation by Injecting Discourse Dependencies](https://mlsanity.com/paper/2209.08626/) (2022)
- [AspectMMKG: A Multi-modal Knowledge Graph with Aspect-aware Entities](https://mlsanity.com/paper/2308.04992/) (2023)
- [Topical Segmentation of Spoken Narratives: A Test Case on Holocaust Survivor Testimonies](https://mlsanity.com/paper/2210.13783/) (2022)
- [Topic Shift Detection in Chinese Dialogues: Corpus and Benchmark](https://mlsanity.com/paper/2305.01195/) (2023)
- [BBScore: A Brownian Bridge Based Metric for Assessing Text Coherence](https://mlsanity.com/paper/2312.16893/) (2023)
- [Topic Segmentation Model Focusing on Local Context](https://mlsanity.com/paper/2301.01935/) (2023)
- [Advancing Topic Segmentation and Outline Generation in Chinese Texts: The Paragraph-level Topic Representation, Corpus, and Benchmark](https://mlsanity.com/paper/2305.14790/) (2023)
- [Self-supervised Answer Retrieval on Clinical Notes](https://mlsanity.com/paper/2108.00775/) (2021)
- [Leveraging Collection-Wide Similarities for Unsupervised Document Structure Extraction](https://mlsanity.com/paper/2402.13906/) (2024)
- [CrossFormer: Cross-Segment Semantic Fusion for Document Segmentation](https://mlsanity.com/paper/2503.23671/) (2025)
- [Reinforced Approximate Exploratory Data Analysis](https://mlsanity.com/paper/2212.06225/) (2022)
- [Prospector Heads: Generalized Feature Attribution for Large Models & Data](https://mlsanity.com/paper/2402.11729/) (2024)

## Abstract (arXiv)

When searching for information, a human reader first glances over a document, spots relevant sections and then focuses on a few sentences for resolving her intention. However, the high variance of document structure complicates to identify the salient topic of a given section at a glance. To tackle this challenge, we present SECTOR, a model to support machine reading systems by segmenting documents into coherent sections and assigning topic labels to each section. Our deep neural network architecture learns a latent topic embedding over the course of a document. This can be leveraged to classify local topics from plain text and segment a document at topic shifts. In addition, we contribute WikiSection, a publicly available dataset with 242k labeled sections in English and German from two distinct domains: diseases and cities. From our extensive evaluation of 20 architectures, we report a highest score of 71.6% F1 for the segmentation and classification of 30 topics from the English city domain, scored by our SECTOR LSTM model with bloom filter embeddings and bidirectional segmentation. This is a significant improvement of 29.5 points F1 compared to state-of-the-art CNN classifiers with baseline segmentation.