  Big data has found applications in multiple domains. One of the largest
sources of textual big data is scientific documents and papers. Big scholarly
data have been used in numerous ways to create innovative applications such as
collaborator discovery, expert finding and research management systems. With
the advent of advanced machine and deep learning techniques, the accuracy and
novelty of such applications have risen manifold. However, the biggest
challenge in the development of deep learning models for scholarly applications
in cloud based environment is the underutilization of resources because of the
excessive time taken by textual preprocessing. This paper presents a
preprocessing pipeline that makes use of Spark for data ingestion and Spark ML
for pipelining preprocessing tasks. The evaluation of the proposed work is done
using a case study, which uses LSTM based text summarization for generating
title or summary from abstract of any research. The ingestion, preprocessing
and cumulative time for the proposed approach (P3SAPP) is much lower than the
conventional approach (CA), which manifests in reduction of costs as well.
