@inproceedings{laban-etal-2022-discord,
title = "Discord Questions: A Computational Approach To Diversity Analysis in News Coverage",
author = "Laban, Philippe and
Wu, Chien-Sheng and
Murakhovs{'}ka, Lidiya and
Chen, Xiang and
Xiong, Caiming",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.380",
doi = "10.18653/v1/2022.findings-emnlp.380",
pages = "5180--5194",
abstract = "There are many potential benefits to news readers accessing diverse sources. Modern news aggregators do the hard work of organizing the news, offering readers a plethora of source options, but choosing which source to read remains challenging. We propose a new framework to assist readers in identifying source differences and gaining an understanding of news coverage diversity. The framework is based on the generation of Discord Questions: questions with a diverse answer pool, explicitly illustrating source differences. To assemble a prototype of the framework, we focus on two components: (1) discord question generation, the task of generating questions answered differently by sources, for which we propose an automatic scoring method, and create a model that improves performance from current question generation (QG) methods by 5{\%}, (2) answer consolidation, the task of grouping answers to a question that are semantically similar, for which we collect data and repurpose a method that achieves 81{\%} balanced accuracy on our realistic test set. We illustrate the framework{'}s feasibility through a prototype interface. Even though model performance at discord QG still lags human performance by more than 15{\%}, generated questions are judged to be more interesting than factoid questions and can reveal differences in the level of detail, sentiment, and reasoning of sources in news coverage. Code is available at \url{https://github.com/Salesforce/discord_questions}.",
}
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<abstract>There are many potential benefits to news readers accessing diverse sources. Modern news aggregators do the hard work of organizing the news, offering readers a plethora of source options, but choosing which source to read remains challenging. We propose a new framework to assist readers in identifying source differences and gaining an understanding of news coverage diversity. The framework is based on the generation of Discord Questions: questions with a diverse answer pool, explicitly illustrating source differences. To assemble a prototype of the framework, we focus on two components: (1) discord question generation, the task of generating questions answered differently by sources, for which we propose an automatic scoring method, and create a model that improves performance from current question generation (QG) methods by 5%, (2) answer consolidation, the task of grouping answers to a question that are semantically similar, for which we collect data and repurpose a method that achieves 81% balanced accuracy on our realistic test set. We illustrate the framework’s feasibility through a prototype interface. Even though model performance at discord QG still lags human performance by more than 15%, generated questions are judged to be more interesting than factoid questions and can reveal differences in the level of detail, sentiment, and reasoning of sources in news coverage. Code is available at https://github.com/Salesforce/discord_questions.</abstract>
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%0 Conference Proceedings
%T Discord Questions: A Computational Approach To Diversity Analysis in News Coverage
%A Laban, Philippe
%A Wu, Chien-Sheng
%A Murakhovs’ka, Lidiya
%A Chen, Xiang
%A Xiong, Caiming
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F laban-etal-2022-discord
%X There are many potential benefits to news readers accessing diverse sources. Modern news aggregators do the hard work of organizing the news, offering readers a plethora of source options, but choosing which source to read remains challenging. We propose a new framework to assist readers in identifying source differences and gaining an understanding of news coverage diversity. The framework is based on the generation of Discord Questions: questions with a diverse answer pool, explicitly illustrating source differences. To assemble a prototype of the framework, we focus on two components: (1) discord question generation, the task of generating questions answered differently by sources, for which we propose an automatic scoring method, and create a model that improves performance from current question generation (QG) methods by 5%, (2) answer consolidation, the task of grouping answers to a question that are semantically similar, for which we collect data and repurpose a method that achieves 81% balanced accuracy on our realistic test set. We illustrate the framework’s feasibility through a prototype interface. Even though model performance at discord QG still lags human performance by more than 15%, generated questions are judged to be more interesting than factoid questions and can reveal differences in the level of detail, sentiment, and reasoning of sources in news coverage. Code is available at https://github.com/Salesforce/discord_questions.
%R 10.18653/v1/2022.findings-emnlp.380
%U https://aclanthology.org/2022.findings-emnlp.380
%U https://doi.org/10.18653/v1/2022.findings-emnlp.380
%P 5180-5194
Markdown (Informal)
[Discord Questions: A Computational Approach To Diversity Analysis in News Coverage](https://aclanthology.org/2022.findings-emnlp.380) (Laban et al., Findings 2022)
ACL