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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 201210 of 359 papers

TitleStatusHype
Rough Set based Aggregate Rank Measure & its Application to Supervised Multi Document Summarization0
Sentence Similarity based on Dependency Tree Kernels for Multi-document Summarization0
Sentential Paraphrase Generation for Agglutinative Languages Using SVM with a String Kernel0
SentTopic-MultiRank: a Novel Ranking Model for Multi-Document Summarization0
SgSum: Transforming Multi-document Summarization into Sub-graph Selection0
SKT5SciSumm -- Revisiting Extractive-Generative Approach for Multi-Document Scientific Summarization0
Solar Cell Surface Defect Inspection Based on Multispectral Convolutional Neural Network0
Sort Story: Sorting Jumbled Images and Captions into Stories0
Subtopic-driven Multi-Document Summarization0
Summarization of Historical Articles Using Temporal Event Clustering0
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