Accepted Papers

In high-stakes information domains such as healthcare, where large language models (LLMs) can produce hallucinations or misinformation, retrieval-augmented generation (RAG) has been proposed as a mitigation strategy, grounding model outputs in external, domain-specific documents. Yet, this approach ...

Integrating heterogeneous modalities for effective information access remains a central challenge in Information Retrieval (IR), particularly in reader-aware summarization, where user perspectives must be incorporated alongside textual and multimedia content. In this work, we present a novel augment...

Prior research on fine-grained complaint analysis haslargely focused on short, context-limited inputs such astweets or product reviews. In this work, we advance thefield by introducing a paradigm that leverages multi-turncustomer-support dialogues as a richer medium for capturingnuanced dissatisfact...

Reader-aware summarization distills articles while embedding user opinions and contextual grounding, shaping results that resonate with diverse readers and ease the challenge of extracting meaning from abundant news sources. However, research so far has centered on English and Chinese, with the comp...