Faithful by Construction: Claim-Anchored Attribution for Multi-Document Summarization

The article presents the Claim-Anchored Multi-document Summarization (CAMS) framework, which enhances multi-document summarization by providing fine-grained attribution and reducing hallucination in large language models (LLMs). CAMS operates through a modular Extract-Select-Rewrite process that extracts atomic claims with token-level provenance, clusters them, and rewrites summaries with clear links to source documents, achieving significant improvements in faithfulness and citation precision—lifting multi-source attribution accuracy by approximately 66%. This framework is crucial for practitioners as it offers a structured approach to ensure factual integrity in generated summaries, addressing common issues with traditional end-to-end LLMs.