With distilling the most important information from a

With the rapid growth of the information in the web data the need to retrieve, anal-
yse and understand a large amount of information has been increased. Huge amount of
data is being generated and consumed by the people and machines every second. Access
to huge amount of information often leads to confusion in identication of the core
idea of that information. Understanding large text documents and found crucial infor-
mation out of it is often a laborious and time-consuming task. So, there is need of the
automatic text summarization to understand the salient or core meaning of the original
text. Text summarization is the process of distilling the most important information
from a source to produce a concise summary. Text summarization has two broad ap-
proaches: extractive and abstractive. Extractive methods aim to select salient phrases,
sentences or paragraphs from the text while abstractive methods focus on generating
summaries from scratch without the constraints of reusing phrases from the original
text. Abstractive summarization is better than extractive summarization because ab-
stractive methods generate a novel words in the summary. The majority of work done
in abstractive summarization is based on a traditional approach, where the features are
manually compiled. The neural network based approach does not rely on compiled fea-
tures. Usually long news article contains large amount of information. Many times
due to lack of time, people cannot read the whole news article. Therefore, the headline
is required in order to get core or complete idea of news without reading the whole news
article. In this work, we create a news headline from the Gujarati news article using
neural network based abstractive summarization method


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