LongDocFACTScore: Evaluating the Factuality of Long Document Abstractive Summarisation

Maintaining factual consistency is a critical issue in abstractive text summarisation, however, it cannot be assessed by traditional automatic metrics used for evaluating text summarisation, such as ROUGE scoring. Recent efforts have been devoted to developing improved metrics for measuring factual consistency using pre-trained language models, but these metrics have restrictive token limits, and are therefore not suitable for evaluating long document text summarisation. Moreover, there is limited research evaluating whether existing automatic evaluation metrics are fit for purpose when applied to long document data sets. In this work, we evaluate the efficacy of automatic metrics at assessing factual consistency in long document text summarisation and propose a new evaluation framework LongDocFACTScore. This framework allows metrics to be extended to any length document. This framework outperforms existing state-of-the-art metrics in its ability to correlate with human measures of factuality when used to evaluate long document summarisation data sets. Furthermore, we show LongDocFACTScore has performance comparable to state-of-the-art metrics when evaluated against human measures of factual consistency on short document data sets. We make our code and annotated data publicly available: https://github.com/jbshp/LongDocFACTScore.