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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 6170 of 359 papers

TitleStatusHype
Fast Concept Mention Grouping for Concept Map-based Multi-Document SummarizationCode0
From Single to Multi: How LLMs Hallucinate in Multi-Document SummarizationCode0
Extending Multi-Document Summarization Evaluation to the Interactive SettingCode0
Estimating Optimal Context Length for Hybrid Retrieval-augmented Multi-document SummarizationCode0
Extending Multi-Text Sentence Fusion Resources via Pyramid AnnotationsCode0
A Hierarchical Encoding-Decoding Scheme for Abstractive Multi-document SummarizationCode0
Extractive Multi-document Summarization using K-means, Centroid-based Method, MMR, and Sentence PositionCode0
ELSKE: Efficient Large-Scale Keyphrase ExtractionCode0
DESCGEN: A Distantly Supervised Datasetfor Generating Entity DescriptionsCode0
A General Optimization Framework for Multi-Document Summarization Using Genetic Algorithms and Swarm IntelligenceCode0
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