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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 341350 of 359 papers

TitleStatusHype
Shaping Political Discourse using multi-source News SummarizationCode0
Pre-training Meets Clustering: A Hybrid Extractive Multi-document Summarization ModelCode0
Auto-hMDS: Automatic Construction of a Large Heterogeneous Multilingual Multi-Document Summarization CorpusCode0
Multi-Document Summarization with Centroid-Based PretrainingCode0
Centroid-based Text Summarization through Compositionality of Word EmbeddingsCode0
Abstractive Multi-Document Summarization via Joint Learning with Single-Document SummarizationCode0
Global-aware Beam Search for Neural Abstractive SummarizationCode0
Bringing Structure into Summaries: Crowdsourcing a Benchmark Corpus of Concept MapsCode0
A Temporally Sensitive Submodularity Framework for Timeline SummarizationCode0
Text Generation: A Systematic Literature Review of Tasks, Evaluation, and ChallengesCode0
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