Rank Your Summaries: Enhancing Bengali Text Summarization via
  Ranking-based Approach
G. M. Shahariar Tonmoy Talukder Rafin Alam Khan Sotez Md. Tanvir Rouf Shawon
With the increasing need for text summarization techniques that are both
efficient and accurate, it becomes crucial to explore avenues that enhance the
quality and precision of pre-trained models specifically tailored for
summarizing Bengali texts. When it comes to text summarization tasks, there are
numerous pre-trained transformer models at one's disposal. Consequently, it
becomes quite a challenge to discern the most informative and relevant summary
for a given text among the various options generated by these pre-trained
summarization models. This paper aims to identify the most accurate and
informative summary for a given text by utilizing a simple but effective
ranking-based approach that compares the output of four different pre-trained
Bengali text summarization models. The process begins by carrying out
preprocessing of the input text that involves eliminating unnecessary elements
such as special characters and punctuation marks. Next, we utilize four
pre-trained summarization models to generate summaries, followed by applying a
text ranking algorithm to identify the most suitable summary. Ultimately, the
summary with the highest ranking score is chosen as the final one. To evaluate
the effectiveness of this approach, the generated summaries are compared
against human-annotated summaries using standard NLG metrics such as BLEU,
ROUGE, BERTScore, WIL, WER, and METEOR. Experimental results suggest that by
leveraging the strengths of each pre-trained transformer model and combining
them using a ranking-based approach, our methodology significantly improves the
accuracy and effectiveness of the Bengali text summarization.