mlsanityData to 7 Oct 2026

Recent Trends in Linear Text Segmentation: a Survey

arXiv 2411.16613 ↗First posted 25 Nov 2024cs.CLcited by 6

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Abstract (arXiv)

Linear Text Segmentation is the task of automatically tagging text documents with topic shifts, i.e. the places in the text where the topics change. A well-established area of research in Natural Language Processing, drawing from well-understood concepts in linguistic and computational linguistic research, the field has recently seen a lot of interest as a result of the surge of text, video, and audio available on the web, which in turn require ways of summarising and categorizing the mole of content for which linear text segmentation is a fundamental step. In this survey, we provide an extensive overview of current advances in linear text segmentation, describing the state of the art in terms of resources and approaches for the task. Finally, we highlight the limitations of available resources and of the task itself, while indicating ways forward based on the most recent literature and under-explored research directions.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 17 of its 51 references that are in the map. First posted on arXiv 25 Nov 2024.

Builds on

17 of 51 references.

References · 17

  1. 2021QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarizationcited by 363
  2. 2018Text Segmentation as a Supervised Learning Taskcited by 76
  3. 2020Text Segmentation by Cross Segment Attentioncited by 54
  4. 2019SECTOR: A Neural Model for Coherent Topic Segmentation and Classificationcited by 48
  5. 2020Two-Level Transformer and Auxiliary Coherence Modeling for Improved Text Segmentationcited by 41
  6. 2022Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topicscited by 35
  7. 2021Unsupervised Topic Segmentation of Meetings with BERT Embeddingscited by 30
  8. 2018Attention-based Neural Text Segmentationcited by 26
  9. 2023Uncovering the Potential of ChatGPT for Discourse Analysis in Dialogue: An Empirical Studycited by 24
  10. 2021TIAGE: A Benchmark for Topic-Shift Aware Dialog Modelingcited by 22
  11. 2021Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherencecited by 22
  12. 2020Improving Context Modeling in Neural Topic Segmentationcited by 21
5 more
  1. 2023Improving Long Document Topic Segmentation Models With Enhanced Coherence Modelingcited by 15
  2. 2021Sequence Model with Self-Adaptive Sliding Window for Efficient Spoken Document Segmentationcited by 14
  3. 2023SuperDialseg: A Large-scale Dataset for Supervised Dialogue Segmentationcited by 10
  4. 2022Structured Summarization: Unified Text Segmentation and Segment Labeling as a Generation Taskcited by 6
  5. 2023MUG: A General Meeting Understanding and Generation Benchmarkcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 4.

6 papers.

  1. 2025Chunk Twice, Embed Once: A Systematic Study of Segmentation and Representation Trade-offs in Chemistry-Aware Retrieval-Augmented Generationcited by 5
  2. 2025Paragraph Segmentation Revisited: Towards a Standard Task for Structuring Speechcited by 2
  3. 2026Linear Semantic Segmentation for Low-Resource Spoken Dialectscited by 1
  4. 2026SegNSP: Revisiting Next Sentence Prediction for Linear Text Segmentationcited by 0
  5. 2026NLP for Local Governance Meeting Records: A Focus Article on Tasks, Datasets, Metrics and Benchmarkcited by 0
  6. 2026Intent-Driven Dynamic Chunking: Segmenting Documents to Reflect Predicted Information Needscited by 0