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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 151160 of 359 papers

TitleStatusHype
Graph-Based Approach to Recognizing CST Relations in Polish Texts0
Graph-based Neural Multi-Document Summarization0
Fear the REAPER: A System for Automatic Multi-Document Summarization with Reinforcement Learning0
Hierarchical Summarization: Scaling Up Multi-Document Summarization0
Complex Question Answering: Unsupervised Learning Approaches and Experiments0
Highlight-Transformer: Leveraging Key Phrase Aware Attention to Improve Abstractive Multi-Document Summarization0
A Preliminary Study of Tweet Summarization using Information Extraction0
Affinity-Preserving Random Walk for Multi-Document Summarization0
Dependency Structure for News Document Summarization0
Interactive Abstractive Summarization for Event News Tweets0
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