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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 76–100 of 359 papers

TitleStatusHype
LLM Based Multi-Document Summarization Exploiting Main-Event Biased Monotone Submodular Content Extraction—0
Multi-document Summarization: A Comparative Evaluation—0
Unsupervised Multi-document Summarization with Holistic Inference—0
Absformer: Transformer-based Model for Unsupervised Multi-Document Abstractive Summarization—0
Pre-training Meets Clustering: A Hybrid Extractive Multi-document Summarization ModelCode0
Revisiting Sentence Union Generation as a Testbed for Text ConsolidationCode0
Automated Metrics for Medical Multi-Document Summarization Disagree with Human EvaluationsCode0
A Hierarchical Encoding-Decoding Scheme for Abstractive Multi-document SummarizationCode0
LBMT team at VLSP2022-Abmusu: Hybrid method with text correlation and generative models for Vietnamese multi-document summarization—0
XWikiGen: Cross-lingual Summarization for Encyclopedic Text Generation in Low Resource LanguagesCode0
Compressed Heterogeneous Graph for Abstractive Multi-Document SummarizationCode0
Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges—0
Mining both Commonality and Specificity from Multiple Documents for Multi-Document Summarization—0
PDSum: Prototype-driven Continuous Summarization of Evolving Multi-document Sets StreamCode0
Generating a Structured Summary of Numerous Academic Papers: Dataset and MethodCode0
Do Multi-Document Summarization Models Synthesize?—0
Open Domain Multi-document Summarization: A Comprehensive Study of Model Brittleness under Retrieval—0
SumREN: Summarizing Reported Speech about Events in NewsCode0
Combining State-of-the-Art Models with Maximal Marginal Relevance for Few-Shot and Zero-Shot Multi-Document Summarization—0
Moving beyond word lists: towards abstractive topic labels for human-like topics of scientific documents—0
How "Multi" is Multi-Document Summarization?Code0
Evaluating Pre-Trained Language Models on Multi-Document Summarization for Literature Reviews—0
Exploring the limits of a base BART for multi-document summarization in the medical domain—0
Overview of the Third Workshop on Scholarly Document Processing—0
基于实体信息增强及多粒度融合的多文档摘要(Multi-Document Summarization Based on Entity Information Enhancement and Multi-Granularity Fusion)—0
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