In the contemporary world, utilization of digital content has risen exponentially. For example, newspaper and web
articles, status updates, advertisements etc. have become an integral part of our daily routine. Thus, there is a need to build
an automated system to summarize such large documents of text in order to save time and effort. Although, there are
summarizers for languages such as English since the work has started in the 1950s and at present has led it up to a matured
stage but there are several languages that still need special attention such as Punjabi language. The Punjabi language is
highly rich in morphological structure as compared to English and other foreign languages. In this work, we provide three
phase extractive summarization methodology using neural networks. It induces compendious summary of Punjabi single text
document. The methodology incorporates pre-processing phase that cleans the text; processing phase that extracts statistical
and linguistic features; and classification phase. The classification based neural network applies an activation function-
sigmoid and weighted error reduction-gradient descent optimization to generate the resultant output summary. The proposed
summarization system is applied over monolingual Punjabi text corpus from Indian languages corpora initiative phase-II.
The precision, recall and F-measure are achieved as 90.0%, 89.28% an 89.65% respectively which is reasonably good in
comparison to the performance of other existing Indian languages" summarizers.This research is partially funded by the Ministry of Economy, Industry and Competitiveness, Spain (CSO2017-86747-R)
