We present a novel Web Pages Summarizer ContextSummarizer that subgroups the given Web pages into ‘sense-clusters’ respecting a user’s topic interests, and constructs a dynamic extractive summary for each sense-cluster. A user’s topic interest is described by the user who selects and refines some of word senses disambiguated within the content contexts of the given Web pages. The semantic simil...

An examination of the program of the next meeting of distillation and absorption indicates there are only a few lectures on thermodynamcis. The authors asked some important companies to give their view of the trends in thermodynamics. This plenary lecture concerns some of the remaining challenges in the thermodynamics related to distillation: data banks and locations for measuring experimental ...

Although human-written summaries of documents tend to involve significant edits to the source text, most automated summarizers are extractive and select sentences verbatim. In this work we examine how elementary discourse units (EDUs) from Rhetorical Structure Theory can be used to extend extractive summarizers to produce a wider range of human-like summaries. Our analysis demonstrates that EDU...

We studied outlier document filtering (ODF) for extractive sentence summarization. Our results are superior compared to the average of the participant systems’ using DUC 2006. Furthermore, we add extractive paragraph summarization to the same system. It is surprising that the results are nearly the same for ROUGE metrics. Although extractive paragraph summarization has a better performance for ...

BACKGROUND Traditional submerged fermentation mainly accumulates intracellular orange pigments with absorption maxima at 470 nm, whereas extractive fermentation of Monascus spp. with Triton X-100 can promote the export of intracellular pigments to extracellular broth, mainly obtaining extracellular yellow pigments with absorption maxima at approximately 410 nm. In this study, a strain of Monasc...

Automatic text summarization is the process of reducing the text content and retaining the important points of the document. Generally, there are two approaches for automatic text summarization: Extractive and Abstractive. The process of extractive based text summarization can be divided into two phases: pre-processing and processing. In this paper, we discuss some of the extractive based text ...