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 201–225 of 359 papers

TitleStatusHype
Extractive Summarization using Continuous Vector Space Models—0
Extract with Order for Coherent Multi-Document Summarization—0
Fast and Robust Compressive Summarization with Dual Decomposition and Multi-Task Learning—0
Fast Joint Compression and Summarization via Graph Cuts—0
Fear the REAPER: A System for Automatic Multi-Document Summarization with Reinforcement Learning—0
Framework of Automatic Text Summarization Using Reinforcement Learning—0
From TimeLines to StoryLines: A preliminary proposal for evaluating narratives—0
Generating an Overview Report over Many Documents—0
Generating Related Work—0
Generating Supplementary Travel Guides from Social Media—0
Graph-Based Approach to Recognizing CST Relations in Polish Texts—0
Graph-based Neural Multi-Document Summarization—0
Hierarchical Summarization: Scaling Up Multi-Document Summarization—0
Highlight-Transformer: Leveraging Key Phrase Aware Attention to Improve Abstractive Multi-Document Summarization—0
Identifying Helpful Sentences in Product Reviews—0
Improving Multi-documents Summarization by Sentence Compression based on Expanded Constituent Parse Trees—0
Improving Multi-Document Summarization via Text Classification—0
Improving the Estimation of Word Importance for News Multi-Document Summarization—0
Improving Update Summarization via Supervised ILP and Sentence Reranking—0
Dependency Structure for News Document Summarization—0
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 Tweets—0
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
Joint Lifelong Topic Model and Manifold Ranking for Document Summarization—0
Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback—0
Show:102550
← PrevPage 9 of 15Next →

No leaderboard results yet.