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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 221230 of 359 papers

TitleStatusHype
Int\'egration de la similarit\'e entre phrases comme crit\`ere pour le r\'esum\'e multi-document (Integrating sentence similarity as a constraint for multi-document summarization)0
Interactive Abstractive Summarization for Event News Tweets0
基于实体信息增强及多粒度融合的多文档摘要(Multi-Document Summarization Based on Entity Information Enhancement and Multi-Granularity Fusion)0
Joint Lifelong Topic Model and Manifold Ranking for Document Summarization0
Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback0
Joint semantic discourse models for automatic multi-document summarization0
Knapsack Constrained Contextual Submodular List Prediction with Application to Multi-document Summarization0
LAG: LLM agents for Leaderboard Auto Generation on Demanding0
Large-Margin Learning of Submodular Summarization Models0
Large-Scale Multi-Document Summarization with Information Extraction and Compression0
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