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 176–200 of 359 papers

TitleStatusHype
Optimized Event Storyline Generation based on Mixture-Event-Aspect Model—0
Optimizing an Approximation of ROUGE - a Problem-Reduction Approach to Extractive Multi-Document Summarization—0
Overview of MSLR2022: A Shared Task on Multi-document Summarization for Literature Reviews—0
Overview of the Third Workshop on Scholarly Document Processing—0
Overview of the VLSP 2022 -- Abmusu Shared Task: A Data Challenge for Vietnamese Abstractive Multi-document Summarization—0
Parallel Hierarchical Transformer with Attention Alignment for Abstractive Multi-Document Summarization—0
PeerSum: A Peer Review Dataset for Abstractive Multi-document Summarization—0
PoBRL: Optimizing Multi-Document Summarization by Blending Reinforcement Learning Policies—0
Predicting Intervention Approval in Clinical Trials through Multi-Document Summarization—0
Predicting Salient Updates for Disaster Summarization—0
Priberam Compressive Summarization Corpus: A New Multi-Document Summarization Corpus for European Portuguese—0
Privacy-Preserving Multi-Document Summarization—0
Probabilistic Domain Modelling With Contextualized Distributional Semantic Vectors—0
Proceedings of the MultiLing 2013 Workshop on Multilingual Multi-document Summarization—0
Query-Chain Focused Summarization—0
Query-focused Multi-document Summarization: Combining a Novel Topic Model with Graph-based Semi-supervised Learning—0
Query-focused Multi-Document Summarization: Combining a Topic Model with Graph-based Semi-supervised Learning—0
Query Focused Multi-Document Summarization with Distant Supervision—0
Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Dataset—0
Reader-Aware Multi-Document Summarization via Sparse Coding—0
RelationListwise for Query-Focused Multi-Document Summarization—0
Rethinking Transformer-based Multi-document Summarization: An Empirical Investigation—0
Revisiting the Centroid-based Method: A Strong Baseline for Multi-Document Summarization—0
Revisiting the Centroid-based Method: A Strong Baseline for Multi-Document Summarization—0
Revisiting the Evaluation for Cross Document Event Coreference—0
Show:102550
← PrevPage 8 of 15Next →

No leaderboard results yet.