Barker, E., Paramita, M.L., Aker, A. et al. (3 more authors) (2016) The SENSEI Annotated Corpus: Human Summaries of Reader Comment Conversations in On-line News. In: Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue. 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue , 13-15 Sep 2017, Los Angeles, USA. Association for Computational Linguistics , pp. 42-52.
Abstract
Researchers are beginning to explore how to generate summaries of extended argumentative conversations in social media, such as those found in reader comments in on-line news. To date, however, there has been little discussion of what these summaries should be like and a lack of humanauthored exemplars, quite likely because writing summaries of this kind of interchange is so difficult. In this paper we propose one type of reader comment summary – the conversation overview summary – that aims to capture the key argumentative content of a reader comment conversation. We describe a method we have developed to support humans in authoring conversation overview summaries and present a publicly available corpus – the first of its kind – of news articles plus comment sets, each multiply annotated, according to our method, with conversation overview summaries.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 Association for Computational Linguistics. Licensed on a Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/). |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 15 Jun 2017 14:02 |
Last Modified: | 19 Dec 2022 13:36 |
Published Version: | https://doi.org/10.18653/v1/W16-3605 |
Status: | Published |
Publisher: | Association for Computational Linguistics |
Refereed: | Yes |
Identification Number: | 10.18653/v1/W16-3605 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:117784 |