natural-language-processing-5
Multi-Document Summarization
3260 papers • 126 benchmarks • 313 datasets
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
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Bottom-Up Abstractive Summarization
Yuntian Deng, Alexander M. Rush, Sebastian Gehrmann•Tue Jul 31 2018
This work explores the use of data-efficient content selectors to over-determine phrases in a source document that should be part of the summary, and shows that this approach improves the ability to compress text, while still generating fluent summaries.
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