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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 271–280 of 359 papers

TitleStatusHype
An Entity-Focused Approach to Generating Company Descriptions—0
A novel extractive multi-document text summarization system using quantum-inspired genetic algorithm: MTSQIGA—0
A Novel Feature-based Bayesian Model for Query Focused Multi-document Summarization—0
Answering Questions from Multiple Documents -- the Role of Multi-Document Summarization—0
Ant Colony System for Multi-Document Summarization—0
An Unsupervised Multi-Document Summarization Framework Based on Neural Document Model—0
基於基因演算法的組合式多文件摘要方法 (An Ensemble Approach for Multi-document Summarization using Genetic Algorithms) [In Chinese]—0
A Preliminary Study of Tweet Summarization using Information Extraction—0
A Redundancy-Aware Sentence Regression Framework for Extractive Summarization—0
A Repository of Corpora for Summarization—0
Show:102550
← PrevPage 28 of 36Next →

No leaderboard results yet.