Sentence examples for summarization approach from inspiring English sources

In this paper, we describe the design and evaluation of extractive summarization approach to assist the learners with reading difficulties.

Due to the statistical test-based summarization approach, GRIN is able to achieve near linear scalability and is not sensitive to input ordering.

The experimental results demonstrate that our proposed summarization approach is able to generate summaries effectively, and those generated summaries are perceived as helpful to support mobile learning.

Instead, we propose an automatic summarization approach based on the analysis of review articles' internal topic structure to assemble customer concerns.

This paper presents a video summarization approach that automatically extracts and visualizes movie storylines in a static image for the purposes of efficient representation and quick overview.

We have also compared the performance of our image summarization approach with that of six other baseline summarization tools on multiple image sets (ImageNet, NUS-WIDE-SCENE and Event image set).

Experiment results show that the proposed method outperforms state-of-the-art extractive summarization approaches.

The rouge and pyramid scores of the presented summarization approaches are tabulated for the three different cases in the Table 1.

Extractive multi-document summarization approaches aim to obtain the main content of a document collection at the same time that the redundant information is reduced.

As existing summarization approaches inherently assign more weights to the important sentences, our approach predicts the summary sentences that are important as well as readable to the target audience with good accuracy.

Different from the existing summarization approaches centered on sentence ranking and clustering, our approach discovers and extracts salient topics from a set of online reviews and further ranks these topics.