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

Showing 125 of 359 papers

TitleStatusHype
Attribute First, then Generate: Locally-attributable Grounded Text GenerationCode1
Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News ArticlesCode1
ODSum: New Benchmarks for Open Domain Multi-Document SummarizationCode1
Summarizing Multiple Documents with Conversational Structure for Meta-Review GenerationCode1
Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple GranularitiesCode1
Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-AwarenessCode1
PeerSum: A Peer Review Dataset for Abstractive Multi-document SummarizationCode1
Proposition-Level Clustering for Multi-Document SummarizationCode1
Proposition-Level Clustering for Multi-Document SummarizationCode1
LongT5: Efficient Text-To-Text Transformer for Long SequencesCode1
PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document SummarizationCode1
MSˆ2: Multi-Document Summarization of Medical StudiesCode1
PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document SummarizationCode1
HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive SummarizationCode1
HowSumm: A Multi-Document Summarization Dataset Derived from WikiHow ArticlesCode1
SummerTime: Text Summarization Toolkit for Non-expertsCode1
TWAG: A Topic-Guided Wikipedia Abstract GeneratorCode1
Efficiently Summarizing Text and Graph Encodings of Multi-Document ClustersCode1
Transfer Learning for Sequence Generation: from Single-source to Multi-sourceCode1
MS2: Multi-Document Summarization of Medical StudiesCode1
Data Augmentation for Abstractive Query-Focused Multi-Document SummarizationCode1
Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific ArticlesCode1
AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document SummarizationCode1
Quantitative Argument Summarization and Beyond: Cross-Domain Key Point AnalysisCode1
Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement LearningCode1
Show:102550
← PrevPage 1 of 15Next →

No leaderboard results yet.