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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 276–300 of 359 papers

TitleStatusHype
An Unsupervised Multi-Document Summarization Framework Based on Neural Document Model—0
基於基因演算法的組合式多文件摘要方法 (An Ensemble Approach for Multi-document Summarization using Genetic Algorithms) [In Chinese]—0
A Preliminary Study of Tweet Summarization using Information Extraction—0
A Redundancy-Aware Sentence Regression Framework for Extractive Summarization—0
A Repository of Corpora for Summarization—0
A Repository of State of the Art and Competitive Baseline Summaries for Generic News Summarization—0
A Sentence Compression Based Framework to Query-Focused Multi-Document Summarization—0
A Spectral Method for Unsupervised Multi-Document Summarization—0
Assessing the performance of Olelo, a real-time biomedical question answering application—0
A Subjective Logic Framework for Multi-Document Summarization—0
A Supervised Aggregation Framework for Multi-Document Summarization—0
A Unified Retrieval Framework with Document Ranking and EDU Filtering for Multi-document Summarization—0
Automatically Determining a Proper Length for Multi-Document Summarization: A Bayesian Nonparametric Approach—0
Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges—0
Automatic Generation of Related Work Sections in Scientific Papers: An Optimization Approach—0
Automatic Related Work Generation: A Meta Study—0
BASS: Boosting Abstractive Summarization with Unified Semantic Graph—0
Benchmarking LLMs on the Semantic Overlap Summarization Task—0
BERT-VBD: Vietnamese Multi-Document Summarization Framework—0
Beyond Centrality and Structural Features: Learning Information Importance for Text Summarization—0
Can one size fit all?: Measuring Failure in Multi-Document Summarization Domain Transfer—0
Cascaded Attention based Unsupervised Information Distillation for Compressive Summarization—0
CIST System for CL-SciSumm 2016 Shared Task—0
CIST System Report for ACL MultiLing 2013 -- Track 1: Multilingual Multi-document Summarization—0
Coarse-to-Fine Query Focused Multi-Document Summarization—0
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