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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 201–250 of 359 papers

TitleStatusHype
Sentence Similarity based on Dependency Tree Kernels for Multi-document Summarization—0
Sentential Paraphrase Generation for Agglutinative Languages Using SVM with a String Kernel—0
SentTopic-MultiRank: a Novel Ranking Model for Multi-Document Summarization—0
SKT5SciSumm -- Revisiting Extractive-Generative Approach for Multi-Document Scientific Summarization—0
Solar Cell Surface Defect Inspection Based on Multispectral Convolutional Neural Network—0
Sort Story: Sorting Jumbled Images and Captions into Stories—0
Subtopic-driven Multi-Document Summarization—0
Summarization of Historical Articles Using Temporal Event Clustering—0
Summarizing Complex Events: a Cross-Modal Solution of Storylines Extraction and Reconstruction—0
Summarizing News Clusters on the Basis of Thematic Chains—0
Summarizing World Speak : A Preliminary Graph Based Approach—0
SumRecom: A Personalized Summarization Approach by Learning from Users' Feedback—0
Supervised Learning of Automatic Pyramid for Optimization-Based Multi-Document Summarization—0
System Combination for Multi-document Summarization—0
Taking into account Inter-sentence Similarity for Update Summarization—0
TGSum: Build Tweet Guided Multi-Document Summarization Dataset—0
The Multilingual Paraphrase Database—0
The SUMMA Platform: A Scalable Infrastructure for Multi-lingual Multi-media Monitoring—0
The UWB Summariser at Multiling-2013—0
Thread Specific Features are Helpful for Identifying Subjectivity Orientation of Online Forum Threads—0
Topic-based Multi-document Summarization using Differential Evolution forCombinatorial Optimization of Sentences—0
Topic-Centric Unsupervised Multi-Document Summarization of Scientific and News Articles—0
Topic-Guided Abstractive Multi-Document Summarization—0
Topic Models: Accounting Component Structure of Bigrams—0
Towards Abstractive Multi-Document Summarization Using Submodular Function-Based Framework, Sentence Compression and Merging—0
Towards a Neural Network Approach to Abstractive Multi-Document Summarization—0
Towards Automatic Construction of News Overview Articles by News Synthesis—0
Towards Coherent Multi-Document Summarization—0
Towards Generating Personalized Hospitalization Summaries—0
Towards Robust Abstractive Multi-Document Summarization: A Caseframe Analysis of Centrality and Domain—0
Towards Syntax-aware Compositional Distributional Semantic Models—0
UETrice at MEDIQA 2021: A Prosper-thy-neighbour Extractive Multi-document Summarization Model—0
Understanding Position Bias Effects on Fairness in Social Multi-Document Summarization—0
Unsupervised Aspect-Based Multi-Document Abstractive Summarization—0
Unsupervised Multi-document Summarization with Holistic Inference—0
Unsupervised Multi-document Summarization for News Corpus with Key Synonyms and Contextual Embeddings—0
Unsupervised Summarization by Jointly Extracting Sentences and Keywords—0
Update Summarization Based on Co-Ranking with Constraints—0
Using a Keyness Metric for Single and Multi Document Summarisation—0
Using External Resources and Joint Learning for Bigram Weighting in ILP-Based Multi-Document Summarization—0
Using POMDPs for Topic-Focused Multi-Document Summarization (L'utilisation des POMDP pour les r\'esum\'es multi-documents orient\'es par une th\'ematique) [in French]—0
Using Shallow Semantic Parsing and Relation Extraction for Finding Contradiction in Text—0
Using Statistical and Semantic Models for Multi-Document Summarization—0
Utilizing Automatic Predicate-Argument Analysis for Concept Map Mining—0
Vector Space Models for Scientific Document Summarization—0
What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization—0
Tagging Funding Agencies and Grants in Scientific Articles using Sequential Learning Models—0
3M:Multi-document Summarization Considering Main and Minor Relationship—0
Absformer: Transformer-based Model for Unsupervised Multi-Document Abstractive Summarization—0
Abstractive Multi-document Summarization by Partial Tree Extraction, Recombination and Linearization—0
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