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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
CAiRE-COVID: A Question Answering and Query-focused Multi-Document Summarization System for COVID-19 Scholarly Information ManagementCode1
Efficiently Summarizing Text and Graph Encodings of Multi-Document ClustersCode1
AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document SummarizationCode1
Bottom-Up Abstractive SummarizationCode1
Data Augmentation for Abstractive Query-Focused Multi-Document SummarizationCode1
DynE: Dynamic Ensemble Decoding for Multi-Document SummarizationCode1
A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events PortalCode1
Proposition-Level Clustering for Multi-Document SummarizationCode1
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
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