Abstract:
In a scientific community including researchers and students who are
dedicated in reading, experimenting and writing ideas to the research world, they must
refer to a significant number of articles on a daily basis. Digital libraries play a key role
in supplementing scientific articles within online platforms. Due to the much abundance
quantities of such articles presented in various platforms, the searching process tends
toward time consuming, and identifying much related resources among them becomes
again difficult. On the other hand, the majority of the scientific articles are available on
a subscription-based and the online archives show only a document abstract but users
necessitate extra material to determine if the article is extremely relevant or not, even
if it merely provides a quick description. Therefore, this work aims to introduce an
alternative approach to simplify the searching and sorting of the scientific articles for a
digital library where a short subunit of sentences of the subscribed articles will be
provided before purchasing it. To find a best algorithm for the process of summarisation
of scientific articles within a short time and provide a good comprehension of the
scientific document are to be investigated. Summaries from the publicly available
SumPubMed dataset of scientific articles are evaluated using supervised and
unsupervised approaches and manual summaries from them are compared. Text Rank
algorithm performed better than the TF-IDF and K-Means algorithms, and the system
achieved a better result when increasing the content size of the article.
