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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 131140 of 359 papers

TitleStatusHype
Exploiting Category-Specific Information for Multi-Document Summarization0
ExB Text Summarizer0
Evolutionary Hierarchical Dirichlet Process for Timeline Summarization0
Automatically Determining a Proper Length for Multi-Document Summarization: A Bayesian Nonparametric Approach0
Analysis of GraphSum's Attention Weights to Improve the Explainability of Multi-Document Summarization0
Adapting Neural Single-Document Summarization Model for Abstractive Multi-Document Summarization: A Pilot Study0
EventSum: A Large-Scale Event-Centric Summarization Dataset for Chinese Multi-News Documents0
Evaluating Pre-Trained Language Models on Multi-Document Summarization for Literature Reviews0
A Unified Retrieval Framework with Document Ranking and EDU Filtering for Multi-document Summarization0
A Multi-level Annotated Corpus of Scientific Papers for Scientific Document Summarization and Cross-document Relation Discovery0
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