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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 191200 of 359 papers

TitleStatusHype
Query-focused Multi-document Summarization: Combining a Novel Topic Model with Graph-based Semi-supervised Learning0
Query-focused Multi-Document Summarization: Combining a Topic Model with Graph-based Semi-supervised Learning0
Query Focused Multi-Document Summarization with Distant Supervision0
Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Dataset0
Reader-Aware Multi-Document Summarization via Sparse Coding0
RelationListwise for Query-Focused Multi-Document Summarization0
Rethinking Transformer-based Multi-document Summarization: An Empirical Investigation0
Revisiting the Centroid-based Method: A Strong Baseline for Multi-Document Summarization0
Revisiting the Centroid-based Method: A Strong Baseline for Multi-Document Summarization0
Revisiting the Evaluation for Cross Document Event Coreference0
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