@inproceedings{ghinassi-etal-2024-recent,
title = "Recent Trends in Linear Text Segmentation: A Survey",
author = "Ghinassi, Iacopo and
Wang, Lin and
Newell, Chris and
Purver, Matthew",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.174/",
doi = "10.18653/v1/2024.findings-emnlp.174",
pages = "3084--3095",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Recent Trends in Linear Text Segmentation: A Survey
%A Ghinassi, Iacopo
%A Wang, Lin
%A Newell, Chris
%A Purver, Matthew
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F ghinassi-etal-2024-recent
%X 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.
%R 10.18653/v1/2024.findings-emnlp.174
%U https://aclanthology.org/2024.findings-emnlp.174/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.174
%P 3084-3095
Markdown (Informal)
[Recent Trends in Linear Text Segmentation: A Survey](https://aclanthology.org/2024.findings-emnlp.174/) (Ghinassi et al., Findings 2024)
ACL
- Iacopo Ghinassi, Lin Wang, Chris Newell, and Matthew Purver. 2024. Recent Trends in Linear Text Segmentation: A Survey. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 3084–3095, Miami, Florida, USA. Association for Computational Linguistics.