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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
Scaling Multi-Document Event Summarization: Evaluating Compression vs. Full-Text ApproachesCode0
Supervising the Centroid Baseline for Extractive Multi-Document SummarizationCode0
Generating Wikipedia by Summarizing Long SequencesCode0
GenerationPrograms: Fine-grained Attribution with Executable ProgramsCode0
GLIMMER: Incorporating Graph and Lexical Features in Unsupervised Multi-Document SummarizationCode0
GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly ReviewsCode0
GlobeSumm: A Challenging Benchmark Towards Unifying Multi-lingual, Cross-lingual and Multi-document News SummarizationCode0
SupMMD: A Sentence Importance Model for Extractive Summarization using Maximum Mean DiscrepancyCode0
PDSum: Prototype-driven Continuous Summarization of Evolving Multi-document Sets StreamCode0
DESCGEN: A Distantly Supervised Datasetfor Generating Entity DescriptionsCode0
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