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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 110 of 359 papers

TitleStatusHype
Attribute First, then Generate: Locally-attributable Grounded Text GenerationCode1
Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News ArticlesCode1
ODSum: New Benchmarks for Open Domain Multi-Document SummarizationCode1
Summarizing Multiple Documents with Conversational Structure for Meta-Review GenerationCode1
Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple GranularitiesCode1
Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-AwarenessCode1
PeerSum: A Peer Review Dataset for Abstractive Multi-document SummarizationCode1
Proposition-Level Clustering for Multi-Document SummarizationCode1
Proposition-Level Clustering for Multi-Document SummarizationCode1
LongT5: Efficient Text-To-Text Transformer for Long SequencesCode1
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