FactKB: Generalizable Factuality Evaluation using Language Models Enhanced with Factual Knowledge

Shangbin Feng, Vidhisha Balachandran, Yuyang Bai, Yulia Tsvetkov

Submission Type: Regular Long Paper

Submission Track: Summarization

Submission Track 2: NLP Applications

Keywords: factuality evaluation, knowledge bases, summarization

Abstract:

Evaluating the factual consistency of automatically generated summaries is essential for the progress and adoption of reliable summarization systems. Despite recent advances, existing factuality evaluation models are not robust, being especially prone to entity and relation errors in new domains. We propose FactKB---a simple new approach to factuality evaluation that is generalizable across domains, in particular with respect to entities and relations. FactKB is based on language models pretrained using facts extracted from external knowledge bases. We introduce three types of complementary factuality pretraining objectives based on entity-specific facts, facts extracted from auxiliary knowledge about entities, and facts constructed compositionally through knowledge base walks.
The resulting factuality evaluation model achieves state-of-the-art performance on two in-domain news summarization benchmarks as well as on three out-of-domain scientific literature datasets. Further analysis of FactKB shows improved ability to detect erroneous entities and relations in summaries and is robust and easily generalizable across domains.

Submission Number: 4812