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Multi-Document Summarization

Multi-Document Summarization is a process of representing a set of documents with a short piece of text by capturing the relevant information and filtering out the redundant information. Two prominent approaches to Multi-Document Summarization are extractive and abstractive summarization. Extractive summarization systems aim to extract salient snippets, sentences or passages from documents, while abstractive summarization systems aim to concisely paraphrase the content of the documents.

Source: Multi-Document Summarization using Distributed Bag-of-Words Model

Papers

Showing 4150 of 359 papers

TitleStatusHype
Can one size fit all?: Measuring Failure in Multi-Document Summarization Domain Transfer0
Multi2: Multi-Agent Test-Time Scalable Framework for Multi-Document Processing0
LAG: LLM agents for Leaderboard Auto Generation on Demanding0
LM Agents for Coordinating Multi-User Information Gathering0
Scaling Multi-Document Event Summarization: Evaluating Compression vs. Full-Text ApproachesCode0
EventSum: A Large-Scale Event-Centric Summarization Dataset for Chinese Multi-News Documents0
Coverage-based Fairness in Multi-document SummarizationCode0
Fair Summarization: Bridging Quality and Diversity in Extractive SummariesCode0
From Single to Multi: How LLMs Hallucinate in Multi-Document SummarizationCode0
GlobeSumm: A Challenging Benchmark Towards Unifying Multi-lingual, Cross-lingual and Multi-document News SummarizationCode0
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