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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 321330 of 359 papers

TitleStatusHype
Evaluating Pre-Trained Language Models on Multi-Document Summarization for Literature Reviews0
EventSum: A Large-Scale Event-Centric Summarization Dataset for Chinese Multi-News Documents0
Evolutionary Hierarchical Dirichlet Process for Timeline Summarization0
ExB Text Summarizer0
Exploiting Category-Specific Information for Multi-Document Summarization0
Exploiting Timegraphs in Temporal Relation Classification0
Exploiting Timelines to Enhance Multi-document Summarization0
Exploring Text Links for Coherent Multi-Document Summarization0
Open Domain Multi-document Summarization: A Comprehensive Study of Model Brittleness under Retrieval0
Exploring the limits of a base BART for multi-document summarization in the medical domain0
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