Due to increasing use of internet and online technologies or online data, there is vast increase in the electronic documents. When a data is being retrieved from such a huge collection of electronic documents, hundreds and thousands of documents are retrieved. Hence, for user, it is not possible to read all the retrieved documents. Also, these documents contain redundant information. In such si...

Contrastive learning models have achieved great success in unsupervised visual representation learning, which maximize the similarities between feature representations of different views same image, while minimize images. In text summarization, output summary is a shorter form input document and they similar meanings. this paper, we propose contrastive model for supervised abstractive where vie...

Automatic text summarization consists of automatically creating a summary of one or more texts. As for Web pages, unfortunately classical techniques cannot be applied in presence of dynamic contents. In this paper, we propose the adoption of snippets –i.e., page excerpts provided together with user query results by search engines– as a text summarization technique. The study is conducted along ...

Automatic text summarization is one of the research goals of Natural Language Processing which relieves humans from studying each and every line in a text document to understand the underlying concepts in it. Automatic text summarization is aimed to create a brief outline of a given text covering the important points in the text. Automatic text summarization can be generic or query specific. Th...