Sentence examples for summarization frameworks from inspiring English sources

Existing visual attention driven summarization frameworks have high computational cost and memory requirements, as well as a lack of efficiency in accurately perceiving human attention.

This paper proposes a novel document summarization framework based on deep learning model, which has been shown outstanding extraction ability in many real-world applications.

Like most researchers in this field, the extractive summarization framework in used in this work.

By removing redundant and non-informative frames, Mehmood et al. [12] presented the wireless capsule endoscopy video summarization framework, which could improve the resizable computing capacities between the client and the cloud server.

In this paper, we introduce the fractal summarization model for document summarization on handheld devices.

In this paper we adapt scalable statistical techniques to perform this summarization under a predictive framework using a vector space model of documents.

In this paper, a MapReduce framework based summarization method is proposed to generate the summaries from large text collections.

Future work in this direction can be providing the support for multi lingual text summarization over the MapReduce framework in order to facilitate the summary generation from the text document collections available in different languages.

This software package implements a supervised learning approach to training submodular scoring functions for extractive multi-document summarization based on structural SVM framework.

We represented vaccine-symptom pairs as well as the summarization features in Resource Description Framework (RDF).

In this paper, we present a framework for hybrid image summarization in which social images and corresponding textual information are taken as vertices in a hypergraph and the task of image summarization is formulated as the problem of hypergraph partition.