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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
Learning to Score System Summaries for Better Content Selection Evaluation.0
Revisiting the Centroid-based Method: A Strong Baseline for Multi-Document Summarization0
Affinity-Preserving Random Walk for Multi-Document Summarization0
Cascaded Attention based Unsupervised Information Distillation for Compressive Summarization0
Towards Automatic Construction of News Overview Articles by News Synthesis0
Interactive Abstractive Summarization for Event News Tweets0
Revisiting the Centroid-based Method: A Strong Baseline for Multi-Document Summarization0
Extractive Multi Document Summarization using Dynamical Measurements of Complex Networks0
Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Dataset0
Tagging Funding Agencies and Grants in Scientific Articles using Sequential Learning Models0
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