@inproceedings{barrow-etal-2020-joint,
title = "A Joint Model for Document Segmentation and Segment Labeling",
author = "Barrow, Joe and
Jain, Rajiv and
Morariu, Vlad and
Manjunatha, Varun and
Oard, Douglas and
Resnik, Philip",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.29",
doi = "10.18653/v1/2020.acl-main.29",
pages = "313--322",
abstract = "Text segmentation aims to uncover latent structure by dividing text from a document into coherent sections. Where previous work on text segmentation considers the tasks of document segmentation and segment labeling separately, we show that the tasks contain complementary information and are best addressed jointly. We introduce Segment Pooling LSTM (S-LSTM), which is capable of jointly segmenting a document and labeling segments. In support of joint training, we develop a method for teaching the model to recover from errors by aligning the predicted and ground truth segments. We show that S-LSTM reduces segmentation error by 30{\%} on average, while also improving segment labeling.",
}
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<abstract>Text segmentation aims to uncover latent structure by dividing text from a document into coherent sections. Where previous work on text segmentation considers the tasks of document segmentation and segment labeling separately, we show that the tasks contain complementary information and are best addressed jointly. We introduce Segment Pooling LSTM (S-LSTM), which is capable of jointly segmenting a document and labeling segments. In support of joint training, we develop a method for teaching the model to recover from errors by aligning the predicted and ground truth segments. We show that S-LSTM reduces segmentation error by 30% on average, while also improving segment labeling.</abstract>
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%0 Conference Proceedings
%T A Joint Model for Document Segmentation and Segment Labeling
%A Barrow, Joe
%A Jain, Rajiv
%A Morariu, Vlad
%A Manjunatha, Varun
%A Oard, Douglas
%A Resnik, Philip
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F barrow-etal-2020-joint
%X Text segmentation aims to uncover latent structure by dividing text from a document into coherent sections. Where previous work on text segmentation considers the tasks of document segmentation and segment labeling separately, we show that the tasks contain complementary information and are best addressed jointly. We introduce Segment Pooling LSTM (S-LSTM), which is capable of jointly segmenting a document and labeling segments. In support of joint training, we develop a method for teaching the model to recover from errors by aligning the predicted and ground truth segments. We show that S-LSTM reduces segmentation error by 30% on average, while also improving segment labeling.
%R 10.18653/v1/2020.acl-main.29
%U https://aclanthology.org/2020.acl-main.29
%U https://doi.org/10.18653/v1/2020.acl-main.29
%P 313-322
Markdown (Informal)
[A Joint Model for Document Segmentation and Segment Labeling](https://aclanthology.org/2020.acl-main.29) (Barrow et al., ACL 2020)
ACL
- Joe Barrow, Rajiv Jain, Vlad Morariu, Varun Manjunatha, Douglas Oard, and Philip Resnik. 2020. A Joint Model for Document Segmentation and Segment Labeling. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 313–322, Online. Association for Computational Linguistics.