LLM Based Multi-Document Summarization Exploiting Main-Event Biased
  Monotone Submodular Content Extraction
Litton J Kurisinkel Nancy F. Chen
Multi-document summarization is a challenging task due to its inherent
subjective bias, highlighted by the low inter-annotator ROUGE-1 score of 0.4
among DUC-2004 reference summaries. In this work, we aim to enhance the
objectivity of news summarization by focusing on the main event of a group of
related news documents and presenting it coherently with sufficient context.
Our primary objective is to succinctly report the main event, ensuring that the
summary remains objective and informative. To achieve this, we employ an
extract-rewrite approach that incorporates a main-event biased
monotone-submodular function for content selection. This enables us to extract
the most crucial information related to the main event from the document
cluster. To ensure coherence, we utilize a fine-tuned Language Model (LLM) for
rewriting the extracted content into a coherent text. The evaluation using
objective metrics and human evaluators confirms the effectiveness of our
approach, as it surpasses potential baselines, demonstrating excellence in both
content coverage, coherence, and informativeness.