Abstract:
Text summarization plays a major role in natural language processing, especially
in scientific communities like researchers, students, and so on. Due to the number
of scientific publications available online rapidly rising, it takes too much time
to identify the most appropriate, quality, and relevant materials for their search out
of thousands. Therefore, there should be an alternative way to sort out and simplify
the search and get a quality and appropriate document based on our search. The
aim of this work is to generate an online platform for a digital library that provides
a good summary of any scientific document which is subscribed to by the library of
the institution. Therefore, we need to find an appropriate and best suitable text
summarization algorithm out of some state-of-the-art text processing algorithms
such as the Text Rank algorithm, TF-IDF algorithm, and K-Means algorithm, which
have been used in different text processing scenarios. To evaluate and select the
best suitable algorithm, we used a publicly available scientific dataset and manually
generated a summary from the dataset. From the experiments processed, the Text
Rank algorithm performed better than the other algorithms.
