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| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | Generating Fact Checking Summaries for Web Claims |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Event Title | The 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020) |
| Event Type | conference |
| Event Location | online |
| Event Start Date | November 16 - 2020 |
| Event End Date | November 20 - 2020 |
| Place of Publication | USA |
| Publisher | arXiv |
| Abstract Text | We present SUMO, a neural attention-based approach that learns to establish the correctness of textual claims based on evidence in the form of text documents (e.g., news articles or Web documents). SUMO further generates an extractive summary by presenting a diversified set of sentences from the documents that explain its decision on the correctness of the textual claim. Prior approaches to address the problem of fact checking and evidence extraction have relied on simple concatenation of claim and document word embeddings as an input to claim driven attention weight computation. This is done so as to extract salient words and sentences from the documents that help establish the correctness of the claim. However, this design of claim-driven attention fails to capture the contextual information in documents properly. We improve on the prior art by using improved claim and title guided hierarchical attention to model effective contextual cues. We show the efficacy of our approach on political, healthcare, and environmental datasets. |
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| Other Identification Number | merlin-id:19822 |
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