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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 110 of 359 papers

TitleStatusHype
GenerationPrograms: Fine-grained Attribution with Executable ProgramsCode0
Improving Fairness of Large Language Models in Multi-document SummarizationCode0
Ask, Retrieve, Summarize: A Modular Pipeline for Scientific Literature SummarizationCode0
A Unified Retrieval Framework with Document Ranking and EDU Filtering for Multi-document Summarization0
Estimating Optimal Context Length for Hybrid Retrieval-augmented Multi-document SummarizationCode0
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
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