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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

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Showing 81–90 of 359 papers

TitleStatusHype
Entity-Supported Summarization of Biomedical Abstracts—0
Controllable Multi-document Summarization: Coverage & Coherence Intuitive Policy with Large Language Model Based Rewards—0
A Preliminary Study of Tweet Summarization using Information Extraction—0
A Hybrid Approach to Multi-document Summarization of Opinions in Reviews—0
Creation and use of Language Resources in a Question-Answering eHealth System—0
Complex Question Answering: Unsupervised Learning Approaches and Experiments—0
AgreeSum: Agreement-Oriented Multi-Document Summarization—0
Deconstructing Human Literature Reviews -- A Framework for Multi-Document Summarization—0
Deep Attentive Sentence Ordering Network—0
Combining State-of-the-Art Models with Maximal Marginal Relevance for Few-Shot and Zero-Shot Multi-Document Summarization—0
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