Abstract
Social media platforms have become new battlegrounds for anti-social
elements, with misinformation being the weapon of choice. Fact-checking
organizations try to debunk as many claims as possible while staying true to
their journalistic processes but cannot cope with its rapid dissemination. We
believe that the solution lies in partial automation of the fact-checking life
cycle, saving human time for tasks which require high cognition. We propose a
new workflow for efficiently detecting previously fact-checked claims that uses
abstractive summarization to generate crisp queries. These queries can then be
executed on a general-purpose retrieval system associated with a collection of
previously fact-checked claims. We curate an abstractive text summarization
dataset comprising noisy claims from Twitter and their gold summaries. It is
shown that retrieval performance improves 2x by using popular out-of-the-box
summarization models and 3x by fine-tuning them on the accompanying dataset
compared to verbatim querying. Our approach achieves Recall@5 and MRR of 35%
and 0.3, compared to baseline values of 10% and 0.1, respectively. Our dataset,
code, and models are available publicly:
https://github.com/varadhbhatnagar/FC-Claim-Det/