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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 7180 of 359 papers

TitleStatusHype
Clustering Sentences with Density Peaks for Multi-document SummarizationCode0
Error Analysis of using BART for Multi-Document Summarization: A Study for English and German LanguageCode0
A General Optimization Framework for Multi-Document Summarization Using Genetic Algorithms and Swarm IntelligenceCode0
Centroid-based Text Summarization through Compositionality of Word EmbeddingsCode0
Extending Multi-Document Summarization Evaluation to the Interactive SettingCode0
Extending Multi-Text Sentence Fusion Resources via Pyramid AnnotationsCode0
Fair Summarization: Bridging Quality and Diversity in Extractive SummariesCode0
Compressed Heterogeneous Graph for Abstractive Multi-Document SummarizationCode0
ELSKE: Efficient Large-Scale Keyphrase ExtractionCode0
Estimating Optimal Context Length for Hybrid Retrieval-augmented Multi-document SummarizationCode0
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