Incremental temporal summarization in multi-party meetings
Ramesh Manuvinakurike, Saurav Sahay, Wenda Chen, Lama Nachman
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.- Anthology ID:
- 2021.sigdial-1.55
- Volume:
- Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue
- Month:
- July
- Year:
- 2021
- Address:
- Singapore and Online
- Editors:
- Haizhou Li, Gina-Anne Levow, Zhou Yu, Chitralekha Gupta, Berrak Sisman, Siqi Cai, David Vandyke, Nina Dethlefs, Yan Wu, Junyi Jessy Li
- Venue:
- SIGDIAL
- SIG:
- SIGDIAL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 530–541
- Language:
- URL:
- https://aclanthology.org/2021.sigdial-1.55
- DOI:
- 10.18653/v1/2021.sigdial-1.55
- Cite (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.
- Cite (Informal):
- Incremental temporal summarization in multi-party meetings (Manuvinakurike et al., SIGDIAL 2021)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/2021.sigdial-1.55.pdf
- Data
- CNN/Daily Mail