SOTAVerified

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 161170 of 359 papers

TitleStatusHype
Improving Multi-documents Summarization by Sentence Compression based on Expanded Constituent Parse Trees0
Affinity-Preserving Random Walk for Multi-Document Summarization0
Large-Margin Learning of Submodular Summarization Models0
Improving the Estimation of Word Importance for News Multi-Document Summarization0
Improving Update Summarization via Supervised ILP and Sentence Reranking0
Dependency Structure for News Document Summarization0
Int\'egration de la similarit\'e entre phrases comme crit\`ere pour le r\'esum\'e multi-document (Integrating sentence similarity as a constraint for multi-document summarization)0
Interactive Abstractive Summarization for Event News Tweets0
基于实体信息增强及多粒度融合的多文档摘要(Multi-Document Summarization Based on Entity Information Enhancement and Multi-Granularity Fusion)0
LBMT team at VLSP2022-Abmusu: Hybrid method with text correlation and generative models for Vietnamese multi-document summarization0
Show:102550
← PrevPage 17 of 36Next →

No leaderboard results yet.