Abstract Summary
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 complex multilingual and multimodal ecosystem of Indian news, shaped by articles, images, and user comments, still largely overlooked. Traditional single large language models (LLMs) often fail to integrate such heterogeneous evidence, yielding shallow or biased outputs. We introduce a Multi-Agent Debate (MAD) framework for reader-aware oriented summarization, built on the COSMMIC dataset, a multilingual, multimodal, and comment-sensitive resource for Indian news. MAD employs role-specialized agents (article analyst, comment integrator, image contextualizer, summary planner, and judge) that deliberate to produce a final summary, accompanied by a justification that attributes information to its source modality. This design not only enhances informativeness and factual consistency but also provides interpretability crucial for trustworthy Information Retrieval (IR) systems. Extensive automatic and human evaluations demonstrate that MAD significantly outperforms strong baselines in generating summaries that are more grounded, diverse, and aligned with reader context, especially in low-resource Indian languages.
