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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 171–180 of 359 papers

TitleStatusHype
Nutri-bullets Hybrid: Consensual Multi-document Summarization—0
On-Demand Distributional Semantic Distance and Paraphrasing—0
On Strategies of Human Multi-Document Summarization—0
On the Effectiveness of using Sentence Compression Models for Query-Focused Multi-Document Summarization—0
On The Feasibility of Open Domain Referring Expression Generation Using Large Scale Folksonomies—0
Optimized Event Storyline Generation based on Mixture-Event-Aspect Model—0
Optimizing an Approximation of ROUGE - a Problem-Reduction Approach to Extractive Multi-Document Summarization—0
Overview of MSLR2022: A Shared Task on Multi-document Summarization for Literature Reviews—0
Overview of the Third Workshop on Scholarly Document Processing—0
Overview of the VLSP 2022 -- Abmusu Shared Task: A Data Challenge for Vietnamese Abstractive Multi-document Summarization—0
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