ARC: Argument Representation and Coverage Analysis for Zero-Shot Long Document Summarization with Instruction Following LLMs
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911412119404544 |
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| author | Elaraby, Mohamed Litman, Diane |
| author_facet | Elaraby, Mohamed Litman, Diane |
| contents | We introduce Argument Representation Coverage (ARC), a bottom-up evaluation framework that assesses how well summaries preserve salient arguments, a crucial issue in summarizing high-stakes domains such as law. ARC provides an interpretable lens by distinguishing between different information types to be covered and by separating omissions from factual errors. Using ARC, we evaluate summaries from eight open-weight large language models in two domains where argument roles are central: long legal opinions and scientific articles. Our results show that while these models capture some salient roles, they frequently omit critical information, particularly when arguments are sparsely distributed across the input. Moreover, ARC uncovers systematic patterns, showing how context window positional bias and role-specific preferences shape argument coverage, and provides actionable guidance for developing more complete and reliable summarization strategies. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_23654 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ARC: Argument Representation and Coverage Analysis for Zero-Shot Long Document Summarization with Instruction Following LLMs Elaraby, Mohamed Litman, Diane Computation and Language We introduce Argument Representation Coverage (ARC), a bottom-up evaluation framework that assesses how well summaries preserve salient arguments, a crucial issue in summarizing high-stakes domains such as law. ARC provides an interpretable lens by distinguishing between different information types to be covered and by separating omissions from factual errors. Using ARC, we evaluate summaries from eight open-weight large language models in two domains where argument roles are central: long legal opinions and scientific articles. Our results show that while these models capture some salient roles, they frequently omit critical information, particularly when arguments are sparsely distributed across the input. Moreover, ARC uncovers systematic patterns, showing how context window positional bias and role-specific preferences shape argument coverage, and provides actionable guidance for developing more complete and reliable summarization strategies. |
| title | ARC: Argument Representation and Coverage Analysis for Zero-Shot Long Document Summarization with Instruction Following LLMs |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.23654 |