@inproceedings{manuvinakurike-etal-2021-incremental,
title = "Incremental temporal summarization in multi-party meetings",
author = "Manuvinakurike, Ramesh and
Sahay, Saurav and
Chen, Wenda and
Nachman, Lama",
editor = "Li, Haizhou and
Levow, Gina-Anne and
Yu, Zhou and
Gupta, Chitralekha and
Sisman, Berrak and
Cai, Siqi and
Vandyke, David and
Dethlefs, Nina and
Wu, Yan and
Li, Junyi Jessy",
booktitle = "Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = jul,
year = "2021",
address = "Singapore and Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.sigdial-1.55",
doi = "10.18653/v1/2021.sigdial-1.55",
pages = "530--541",
abstract = "In this work, we develop a dataset for incremental temporal summarization in a multiparty dialogue. We use crowd-sourcing paradigm with a model-in-loop approach for collecting the summaries and compare the data with the expert summaries. We leverage the question generation paradigm to automatically generate questions from the dialogue, which can be used to validate the user participation and potentially also draw attention of the user towards the contents then need to summarize. We then develop several models for abstractive summary generation in the Incremental temporal scenario. We perform a detailed analysis of the results and show that including the past context into the summary generation yields better summaries.",
}
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%0 Conference Proceedings
%T Incremental temporal summarization in multi-party meetings
%A Manuvinakurike, Ramesh
%A Sahay, Saurav
%A Chen, Wenda
%A Nachman, Lama
%Y Li, Haizhou
%Y Levow, Gina-Anne
%Y Yu, Zhou
%Y Gupta, Chitralekha
%Y Sisman, Berrak
%Y Cai, Siqi
%Y Vandyke, David
%Y Dethlefs, Nina
%Y Wu, Yan
%Y Li, Junyi Jessy
%S Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2021
%8 July
%I Association for Computational Linguistics
%C Singapore and Online
%F manuvinakurike-etal-2021-incremental
%X In this work, we develop a dataset for incremental temporal summarization in a multiparty dialogue. We use crowd-sourcing paradigm with a model-in-loop approach for collecting the summaries and compare the data with the expert summaries. We leverage the question generation paradigm to automatically generate questions from the dialogue, which can be used to validate the user participation and potentially also draw attention of the user towards the contents then need to summarize. We then develop several models for abstractive summary generation in the Incremental temporal scenario. We perform a detailed analysis of the results and show that including the past context into the summary generation yields better summaries.
%R 10.18653/v1/2021.sigdial-1.55
%U https://aclanthology.org/2021.sigdial-1.55
%U https://doi.org/10.18653/v1/2021.sigdial-1.55
%P 530-541
Markdown (Informal)
[Incremental temporal summarization in multi-party meetings](https://aclanthology.org/2021.sigdial-1.55) (Manuvinakurike et al., SIGDIAL 2021)
ACL
- Ramesh Manuvinakurike, Saurav Sahay, Wenda Chen, and Lama Nachman. 2021. Incremental temporal summarization in multi-party meetings. In Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 530–541, Singapore and Online. Association for Computational Linguistics.