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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 211220 of 359 papers

TitleStatusHype
Graph-Based Approach to Recognizing CST Relations in Polish Texts0
Graph-based Neural Multi-Document Summarization0
Hierarchical Summarization: Scaling Up Multi-Document Summarization0
Highlight-Transformer: Leveraging Key Phrase Aware Attention to Improve Abstractive Multi-Document Summarization0
Identifying Helpful Sentences in Product Reviews0
Improving Multi-documents Summarization by Sentence Compression based on Expanded Constituent Parse Trees0
Improving Multi-Document Summarization via Text Classification0
Improving the Estimation of Word Importance for News Multi-Document Summarization0
Improving Update Summarization via Supervised ILP and Sentence Reranking0
Dependency Structure for News Document Summarization0
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