Evaluation frameworks for text summarization have evolved in terms of both domain coverage and metrics. However, existing benchmarks still lack domain-specific assessment criteria, remain predominantly English-centric, and face challenges with human annotation due to the complexity of reasoning. To address these, we introduce MSumBench, which provides a multi-dimensional, multi-domain evaluation of summarization in English and Chinese. It also incorporates specialized assessment criteria for each domain and leverages a multi-agent debate system to enhance annotation quality. By evaluating eight modern summarization models, we discover distinct performance patterns across domains and languages. We further examine large language models as summary evaluators, analyzing the correlation between their evaluation and summarization capabilities, and uncovering systematic bias in their assessment of self-generated summaries. Our benchmark dataset is publicly available at https://github.com/DISL-Lab/MSumBench.

Retrieval-augmented generation (RAG) enhances the quality of LLM generation by providing relevant chunks, but retrieving accurately from external knowledge remains challenging due to missing contextually important words in query. We present Word2Passage, a novel approach that improves retrieval accuracy by optimizing word importance in query expansion. Our method generates references at word, sentence, and passage levels for query expansion, then determines word importance by considering both their reference level origin and characteristics derived from query types and corpus analysis. Specifically, our method assigns distinct importance scores to words based on whether they originate from word, sentence, or passage-level references. Extensive experiments demonstrate that Word2Passage outperforms existing methods across various datasets and LLM configurations, effectively enhancing both retrieval accuracy and generation quality. The code is publicly available at https://github.com/DISL-Lab/Word2Passage