no code implementations • 22 Sep 2023 • Mohsinul Kabir, Mohammed Saidul Islam, Md Tahmid Rahman Laskar, Mir Tafseer Nayeem, M Saiful Bari, Enamul Hoque
Large Language Models (LLMs) have emerged as one of the most important breakthroughs in NLP for their impressive skills in language generation and other language-specific tasks.
Abstractive Text Summarization Natural Language Inference +6
1 code implementation • 22 Feb 2023 • Mir Tafseer Nayeem, Davood Rafiei
Helpful reviews have been essential for the success of e-commerce services, as they help customers make quick purchase decisions and benefit the merchants in their sales.
1 code implementation • EACL 2021 • Radia Rayan Chowdhury, Mir Tafseer Nayeem, Tahsin Tasnim Mim, Md. Saifur Rahman Chowdhury, Taufiqul Jannat
We also provide a human-annotated dataset with document-summary pairs to evaluate our abstractive model and to support the comparison of future abstractive summarization systems of the Bengali Language.
1 code implementation • 9 Dec 2020 • Susmoy Chakraborty, Mir Tafseer Nayeem, Wasi Uddin Ahmad
Determining the readability of a text is the first step to its simplification.
no code implementations • COLING 2018 • Mir Tafseer Nayeem, Tanvir Ahmed Fuad, Yllias Chali
Furthermore, we apply our sentence level model to implement an abstractive multi-document summarization system where documents usually contain a related set of sentences.
no code implementations • IJCNLP 2017 • Yllias Chali, Moin Tanvee, Mir Tafseer Nayeem
We propose a submodular function-based summarization system which integrates three important measures namely importance, coverage, and non-redundancy to detect the important sentences for the summary.
no code implementations • WS 2017 • Mir Tafseer Nayeem, Yllias Chali
In this work, we aim at developing an extractive summarizer in the multi-document setting.