Generating Fact Checking Summaries for Web Claims
This work addresses the challenge of automated fact-checking for web claims, which is crucial for combating misinformation, but it is incremental as it builds on existing neural attention methods with specific enhancements.
The authors tackled the problem of fact-checking web claims by developing SUMO, a neural attention-based approach that generates extractive summaries from evidence documents to explain claim correctness decisions, achieving improved performance over prior methods on datasets covering political, healthcare, and environmental issues.
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 does not 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 datasets concerning political, healthcare, and environmental issues.