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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

Showing 125 of 359 papers

TitleStatusHype
MSˆ2: Multi-Document Summarization of Medical StudiesCode1
Pre-training via ParaphrasingCode1
GameWikiSum: a Novel Large Multi-Document Summarization DatasetCode1
LongT5: Efficient Text-To-Text Transformer for Long SequencesCode1
Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific ArticlesCode1
PeerSum: A Peer Review Dataset for Abstractive Multi-document SummarizationCode1
Efficiently Summarizing Text and Graph Encodings of Multi-Document ClustersCode1
Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News ArticlesCode1
HowSumm: A Multi-Document Summarization Dataset Derived from WikiHow ArticlesCode1
Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-AwarenessCode1
Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement LearningCode1
Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple GranularitiesCode1
CAiRE-COVID: A Question Answering and Query-focused Multi-Document Summarization System for COVID-19 Scholarly Information ManagementCode1
ODSum: New Benchmarks for Open Domain Multi-Document SummarizationCode1
Data Augmentation for Abstractive Query-Focused Multi-Document SummarizationCode1
DynE: Dynamic Ensemble Decoding for Multi-Document SummarizationCode1
AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document SummarizationCode1
Proposition-Level Clustering for Multi-Document SummarizationCode1
Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document SummarizationCode1
HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive SummarizationCode1
A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events PortalCode1
Attribute First, then Generate: Locally-attributable Grounded Text GenerationCode1
Bottom-Up Abstractive SummarizationCode1
MS2: Multi-Document Summarization of Medical StudiesCode1
PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document SummarizationCode1
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