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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 4150 of 359 papers

TitleStatusHype
Extractive Multi-document Summarization using K-means, Centroid-based Method, MMR, and Sentence PositionCode0
A Multi-Document Coverage Reward for RELAXed Multi-Document SummarizationCode0
Fair Summarization: Bridging Quality and Diversity in Extractive SummariesCode0
Extending Multi-Document Summarization Evaluation to the Interactive SettingCode0
Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document SummarizationCode0
Error Analysis of using BART for Multi-Document Summarization: A Study for English and German LanguageCode0
Extending Multi-Text Sentence Fusion Resources via Pyramid AnnotationsCode0
Fast Concept Mention Grouping for Concept Map-based Multi-Document SummarizationCode0
Estimating Optimal Context Length for Hybrid Retrieval-augmented Multi-document SummarizationCode0
Generating Wikipedia by Summarizing Long SequencesCode0
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