The presented work addresses the integrated design of solvent molecules with separation and reactive-separation process systems. The proposed design philosophy relies on extensive structural optimization both at the solvent and process synthesis stage and allows the identification of solvent molecules based on process performance criteria. It employs multi-objective optimization technology in o...

Neural document ranking models perform impressively well due to superior language understanding gained from pre-training tasks. However, their complexity and large number of parameters these (typically transformer-based) are often non-interpretable in that decisions can not be clearly attributed specific parts the input documents. In this article, we propose inherently interpretable by generati...

Abstract Auto-regressive extractive summarization approaches determine sentence extraction probability conditioning on previous decisions by maintaining a partial summary representation. Despite its popularity, the framework has two main drawbacks: 1) representation is irresolutely denoted weighted summation of all processed sentences without any filtering, resulting in noisy and degrading effe...

Automatic text summarization aims to cut down readers’ time and cognitive effort by reducing the content of a document without compromising on its essence. Ergo, informativeness is prime attribute summary generated an algorithm, selecting sentences that capture essence primary goal extractive summarization. In this paper, we employ Shannon’s entropy sentences. We Non-negative Matrix Factorizati...

Automated summarization methods can be defined as “language-independent,” if they are not based on any languagespecific knowledge. Such methods can be used for multilingual summarization defined by Mani (2001) as “processing several languages, with summary in the same language as input.” In this paper, we introduce MUSE, a languageindependent approach for extractive summarization based on the l...

The problem of generating headlines for documents using purely statistical approach has been long standing. We describe here an improved extractive approach based on keywords. The insight here is that if one tries to summarize a document, one will invariably use keywords from the document itself. There are two aspects to the problem namely, finding the relevant set of keywords and finding the p...