Marathi Text Document Summarization using Neural Networks
Authors/Creators
- 1. Alamuri Ratnamala Institute of Engineering, Engineering and Technology, University of Mumbai, Maharashtra, India
- 2. Assistant Prof., Datta Meghe College of Engineering Navi Mumbai, Maharashtra, India
Description
The internet is comprised of web pages, news articles, status updates, blogs, and much more. It is difficult
to navigate through this data as it is unstructured and usually discursive. Manual summarization of large
documents of texts is tedious and error-prone. Also, the results in such kind of summarization may lead to
different results for a particular document. Thus, Automatic text summarization has become important
due to the tremendous growth of information and data. It chooses the most informative part of the text and
forms summaries that reveal the main purpose of the given document. It yields a summary produced by a
summarization system which allows readers to comprehend the content of the document instead of reading
every individual document. So, the overall intention of Text Summarizer is to provide the meaning of the
text in fewer words and sentences. To perform extractive text summarization, we propose to use a
Recurrent Neural Network (RNN) – a type of neural network that can perform calculations on sequential
data (e.g. sequences of words) – as it has become the standard approach for many Natural Language
Processing tasks.