Open-Domain Multi-Document Summarization (ODMDS) is crucial for addressing diverse information needs, which aims to generate a summary as answer to user's query, synthesizing relevant content from multiple documents in a large collection. Existing approaches that first find relevant passages and then generate a summary using a language model are inadequate for ODMDS. This is because open-ended queries often require additional context for the retrieved passages to cover the topic comprehensively, making it challenging to retrieve all relevant passages initially. While iterative retrieval methods have been explored for multi-hop question answering (MQA), they are impractical for ODMDS due to high latency from repeated large language model (LLM) inference for reasoning. To address this issue, we propose LightPAL, a lightweight passage retrieval method for ODMDS that constructs a graph representing passage relationships using an LLM during indexing and employs random walk instead of iterative reasoning and retrieval at inference time. Experiments on ODMDS benchmarks show that LightPAL outperforms baseline retrievers in summary quality while being significantly more efficient than an iterative MQA approach.

提出了一种轻量级的语段检索方法LightPAL，通过使用语言模型构造表示语段关系的图表，在索引期间进行随机游走而非迭代推理和检索，以应对开放领域的多文档摘要生成中存在的问题，并在ODMDS基准测试中表现出优于基准检索器的摘要质量并且比迭代式多跳问题解答方法更加高效。

LightPAL: 开放域多文档摘要的轻量级段落检索