Using Similarity to Evaluate Factual Consistency in Summaries
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929510966886400 |
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| author | Ye, Yuxuan Simpson, Edwin Rodriguez, Raul Santos |
| author_facet | Ye, Yuxuan Simpson, Edwin Rodriguez, Raul Santos |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_15090 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Using Similarity to Evaluate Factual Consistency in Summaries Ye, Yuxuan Simpson, Edwin Rodriguez, Raul Santos Computation and Language 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. |
| title | Using Similarity to Evaluate Factual Consistency in Summaries |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2409.15090 |