Using Similarity to Evaluate Factual Consistency in Summaries

• URL: http://arxiv.org/abs/2409.15090v1
• Date: Mon, 23 Sep 2024 15:02:38 GMT
• Title: Using Similarity to Evaluate Factual Consistency in Summaries
• Authors: Yuxuan Ye, Edwin Simpson, Raul Santos Rodriguez,
• Abstract summary: Abstractive summarisers generate fluent summaries, but the factuality of the generated text is not guaranteed. We propose a new zero-shot factuality evaluation metric, Sentence-BERTScore (SBERTScore), which compares sentences between the summary and the source document. Our experiments indicate that each technique has different strengths, with SBERTScore particularly effective in identifying correct summaries.
• Score: 2.7595794227140056
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Cutting-edge abstractive summarisers generate fluent summaries, but the factuality of the generated text is not guaranteed. Early summary factuality evaluation metrics are usually based on n-gram overlap and embedding similarity, but are reported fail to align with human annotations. Therefore, many techniques for detecting factual inconsistencies build pipelines around natural language inference (NLI) or question-answering (QA) models with additional supervised learning steps. In this paper, we revisit similarity-based metrics, showing that this failure stems from the comparison text selection and its granularity. We propose a new zero-shot factuality evaluation metric, Sentence-BERT Score (SBERTScore), which compares sentences between the summary and the source document. It outperforms widely-used word-word metrics including BERTScore and can compete with existing NLI and QA-based factuality metrics on the benchmark without needing any fine-tuning. Our experiments indicate that each technique has different strengths, with SBERTScore particularly effective in identifying correct summaries. We demonstrate how a combination of techniques is more effective in detecting various types of error.

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