@inproceedings{kano-etal-2018-harnessing,
title = "Harnessing Popularity in Social Media for Extractive Summarization of Online Conversations",
author = "Kano, Ryuji and
Miura, Yasuhide and
Taniguchi, Motoki and
Chen, Yan-Ying and
Chen, Francine and
Ohkuma, Tomoko",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1144",
doi = "10.18653/v1/D18-1144",
pages = "1139--1145",
abstract = "We leverage a popularity measure in social media as a distant label for extractive summarization of online conversations. In social media, users can vote, share, or bookmark a post they prefer. The number of these actions is regarded as a measure of popularity. However, popularity is not determined solely by content of a post, e.g., a text or an image it contains, but is highly based on its contexts, e.g., timing, and authority. We propose Disjunctive model that computes the contribution of content and context separately. For evaluation, we build a dataset where the informativeness of comments is annotated. We evaluate the results with ranking metrics, and show that our model outperforms the baseline models which directly use popularity as a measure of informativeness.",
}
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<abstract>We leverage a popularity measure in social media as a distant label for extractive summarization of online conversations. In social media, users can vote, share, or bookmark a post they prefer. The number of these actions is regarded as a measure of popularity. However, popularity is not determined solely by content of a post, e.g., a text or an image it contains, but is highly based on its contexts, e.g., timing, and authority. We propose Disjunctive model that computes the contribution of content and context separately. For evaluation, we build a dataset where the informativeness of comments is annotated. We evaluate the results with ranking metrics, and show that our model outperforms the baseline models which directly use popularity as a measure of informativeness.</abstract>
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%0 Conference Proceedings
%T Harnessing Popularity in Social Media for Extractive Summarization of Online Conversations
%A Kano, Ryuji
%A Miura, Yasuhide
%A Taniguchi, Motoki
%A Chen, Yan-Ying
%A Chen, Francine
%A Ohkuma, Tomoko
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F kano-etal-2018-harnessing
%X We leverage a popularity measure in social media as a distant label for extractive summarization of online conversations. In social media, users can vote, share, or bookmark a post they prefer. The number of these actions is regarded as a measure of popularity. However, popularity is not determined solely by content of a post, e.g., a text or an image it contains, but is highly based on its contexts, e.g., timing, and authority. We propose Disjunctive model that computes the contribution of content and context separately. For evaluation, we build a dataset where the informativeness of comments is annotated. We evaluate the results with ranking metrics, and show that our model outperforms the baseline models which directly use popularity as a measure of informativeness.
%R 10.18653/v1/D18-1144
%U https://aclanthology.org/D18-1144
%U https://doi.org/10.18653/v1/D18-1144
%P 1139-1145
Markdown (Informal)
[Harnessing Popularity in Social Media for Extractive Summarization of Online Conversations](https://aclanthology.org/D18-1144) (Kano et al., EMNLP 2018)
ACL