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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
Benchmarking LLMs on the Semantic Overlap Summarization Task0
BERT-VBD: Vietnamese Multi-Document Summarization Framework0
Deconstructing Human Literature Reviews -- A Framework for Multi-Document Summarization0
Discriminative Marginalized Probabilistic Neural Method for Multi-Document Summarization of Medical Literature0
A Spectral Method for Unsupervised Multi-Document Summarization0
Abstract Meaning Representation for Multi-Document Summarization0
Complex Question Answering: Unsupervised Learning Approaches and Experiments0
A Sentence Compression Based Framework to Query-Focused Multi-Document Summarization0
A Repository of State of the Art and Competitive Baseline Summaries for Generic News Summarization0
Concept-Map-Based Multi-Document Summarization using Concept Coreference Resolution and Global Importance Optimization0
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