We demonstrate TextRank – a system for unsupervised extractive summarization that relies on the application of iterative graphbased ranking algorithms to graphs encoding the cohesive structure of a text. An important characteristic of the system is that it does not rely on any language-specific knowledge resources or any manually constructed training data, and thus it is highly portable to new ...

Most approaches to extractive summarization define a set of features upon which selection of sentences is based, using algorithms independent of the features themselves. We propose a new set of features based on low-level, atomic events that describe relationships between important actors in a document or set of documents. We investigate the effect this new feature has on extractive summarizati...

We present two novel and contrasting Recurrent Neural Network (RNN) based architectures for extractive summarization of documents. The Classifier based architecture sequentially accepts or rejects each sentence in the original document order for its membership in the final summary. The Selector architecture, on the other hand, is free to pick one sentence at a time in any arbitrary order to pie...

Aims: The purpose is to study Pharmacognostical characteristics, Physicochemical evaluation, Phytochemical and Antimicrobial assay of Curcuma longa leaves extract. Methods Material: Different qualitative tests were performed confirmed the presence phytochemical compounds under consideration followed by quantitative analysis using sophisticated techniques such as spectrophotometric, Thin layer c...