Text Summarization models facilitate biomedical clinicians and researchers in acquiring informative data from enormous domain-specific literature within less time effort. Evaluating selecting the most sentences articles is always challenging. This study aims to develop a dual-mode text summarization model achieve enhanced coverage information. The research also includes checking fitment of appr...

As enormous amount of electronic documents on the Web have been increasing, the necessity of automatic summarization has also been increasing to help people grasp the essential points of the documents. Many summarization techniques dealing with single document and multi-documents have been studied. However, due to the increase of the documents which report the change of topics along a timeline,...

Discourse theories claim that text gets meaning in context. Most summarization systems do not take advantage of this. They assess the relevance of each passage individually rather than modeling the way context affects the relevance of passages. This paper presents a framework for graph-based summarization in order to model relations in text, so that the passages can be viewed in a broader conte...

This paper describes the information extraction systems of PRIS at Temporal Summarization Track. The Temporal Summarization Track includes two tasks: sequential update summarization and value tracking. For the first task, we focus attention on keywords mining and sentence scoring. The system utilizes hierarchical Latent Dirichlet Allocation (LDA) to do keywords mining and score sentences with k...

Knowledge bases built in the knowledge processing field have a problem that experts to add rules or update them through modifications. To solve this problem, research has been conducted on graph expansion methods using deep learning technology, and recent years, many studies of generating by embedding graph’s triple information continuous vector space. In paper, literature summary, we propose d...

How do graph clustering techniques compare in terms of summarization power? How well can they summarize a million-node graph with a few representative structures? In this paper, we compare and contrast different techniques: METIS, LOUVAIN, SPECTRAL CLUSTERING, SLASHBURN, BIGCLAM, HYCOMFIT, and KCBC, our proposed k-core-based clustering method. Unlike prior work that focuses on various measures ...