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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 5160 of 359 papers

TitleStatusHype
A Hierarchical Encoding-Decoding Scheme for Abstractive Multi-document SummarizationCode0
Summarizing Multiple Documents with Conversational Structure for Meta-Review GenerationCode1
LBMT team at VLSP2022-Abmusu: Hybrid method with text correlation and generative models for Vietnamese multi-document summarization0
XWikiGen: Cross-lingual Summarization for Encyclopedic Text Generation in Low Resource LanguagesCode0
Compressed Heterogeneous Graph for Abstractive Multi-Document SummarizationCode0
Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges0
Mining both Commonality and Specificity from Multiple Documents for Multi-Document Summarization0
PDSum: Prototype-driven Continuous Summarization of Evolving Multi-document Sets StreamCode0
Generating a Structured Summary of Numerous Academic Papers: Dataset and MethodCode0
Do Multi-Document Summarization Models Synthesize?0
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