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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 251–300 of 359 papers

TitleStatusHype
CIST System for CL-SciSumm 2016 Shared Task—0
Sentence Similarity based on Dependency Tree Kernels for Multi-document Summarization—0
TGSum: Build Tweet Guided Multi-Document Summarization Dataset—0
Joint semantic discourse models for automatic multi-document summarization—0
On Strategies of Human Multi-Document Summarization—0
A Discursive Grid Approach to Model Local Coherence in Multi-document Summaries—0
Abstractive Multi-document Summarization with Semantic Information Extraction—0
AllSummarizer system at MultiLing 2015: Multilingual single and multi-document summarizationCode0
System Combination for Multi-document Summarization—0
MultiLing 2015: Multilingual Summarization of Single and Multi-Documents, On-line Fora, and Call-center Conversations—0
Extractive Summarization by Aggregating Multiple Similarities—0
Measuring Semantic Similarity for Bengali Tweets Using WordNet—0
ExB Text Summarizer—0
Privacy-Preserving Multi-Document Summarization—0
Extending a Single-Document Summarizer to Multi-Document: a Hierarchical Approach—0
Multi-Document Summarization via Discriminative Summary Reranking—0
Predicting Salient Updates for Disaster Summarization—0
From TimeLines to StoryLines: A preliminary proposal for evaluating narratives—0
End-to-end Argument Generation System in Debating—0
Abstractive Multi-Document Summarization via Phrase Selection and Merging—0
Vector Space Models for Scientific Document Summarization—0
A Method of Accounting Bigrams in Topic Models—0
Improving Update Summarization via Supervised ILP and Sentence Reranking—0
Topic Models: Accounting Component Structure of Bigrams—0
Using External Resources and Joint Learning for Bigram Weighting in ILP-Based Multi-Document Summarization—0
Clustering Sentences with Density Peaks for Multi-document SummarizationCode0
Reader-Aware Multi-Document Summarization via Sparse Coding—0
Topic-based Multi-document Summarization using Differential Evolution forCombinatorial Optimization of Sentences—0
Sentential Paraphrase Generation for Agglutinative Languages Using SVM with a String Kernel—0
Multi-document Summarization Using Bipartite Graphs—0
Fear the REAPER: A System for Automatic Multi-Document Summarization with Reinforcement Learning—0
Improving Multi-documents Summarization by Sentence Compression based on Expanded Constituent Parse Trees—0
Automatic Generation of Related Work Sections in Scientific Papers: An Optimization Approach—0
Analyzing Stemming Approaches for Turkish Multi-Document Summarization—0
Exploiting Timegraphs in Temporal Relation Classification—0
Empirical analysis of exploiting review helpfulness for extractive summarization of online reviews—0
Towards Syntax-aware Compositional Distributional Semantic Models—0
Query-focused Multi-Document Summarization: Combining a Topic Model with Graph-based Semi-supervised Learning—0
Learning to Generate Coherent Summary with Discriminative Hidden Semi-Markov Model—0
Generating Supplementary Travel Guides from Social Media—0
Exploiting Timelines to Enhance Multi-document Summarization—0
A Hybrid Approach to Multi-document Summarization of Opinions in Reviews—0
Detection of Topic and its Extrinsic Evaluation Through Multi-Document Summarization—0
Hierarchical Summarization: Scaling Up Multi-Document Summarization—0
Multi-document summarization using distortion-rate ratio—0
Query-Chain Focused Summarization—0
Multi-layered graph-based multi-document summarization model—0
Summarizing News Clusters on the Basis of Thematic Chains—0
LQVSumm: A Corpus of Linguistic Quality Violations in Multi-Document Summarization—0
Extracting News Web Page Creation Time with DCTFinder—0
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