1 code implementation • LREC 2022 • Ashok Urlana, Nirmal Surange, Pavan Baswani, Priyanka Ravva, Manish Shrivastava
But with this work, we show that even with a crowd sourced summary generation approach, quality can be controlled by aggressive expert informed filtering and sampling-based human evaluation.
1 code implementation • sdp (COLING) 2022 • Ashok Urlana, Nirmal Surange, Manish Shrivastava
The MuP-2022 shared task focuses on multiperspective scientific document summarization.
no code implementations • 25 Mar 2024 • Ashok Urlana, Aditya Saibewar, Bala Mallikarjunarao Garlapati, Charaka Vinayak Kumar, Ajeet Kumar Singh, Srinivasa Rao Chalamala
In this paper, we present our methods for the SemEval2024 Task8, aiming to detect machine-generated text across various domains in both mono-lingual and multi-lingual contexts.
no code implementations • 22 Mar 2024 • Abdur Rahman Bin Md Faizullah, Ashok Urlana, Rahul Mishra
Examining limitations is a crucial step in the scholarly research reviewing process, revealing aspects where a study might lack decisiveness or require enhancement.
no code implementations • 22 Feb 2024 • Ashok Urlana, Charaka Vinayak Kumar, Ajeet Kumar Singh, Bala Mallikarjunarao Garlapati, Srinivasa Rao Chalamala, Rahul Mishra
Large language models (LLMs) have become the secret ingredient driving numerous industrial applications, showcasing their remarkable versatility across a diverse spectrum of tasks.
no code implementations • 22 Dec 2023 • Nikhilesh Bhatnagar, Ashok Urlana, Vandan Mujadia, Pruthwik Mishra, Dipti Misra Sharma
We analyze the data and propose methods to match articles to video descriptions that serve as document and summary pairs.
1 code implementation • 15 Nov 2023 • Ashok Urlana, Pruthwik Mishra, Tathagato Roy, Rahul Mishra
Generic text summarization approaches often fail to address the specific intent and needs of individual users.
no code implementations • 15 Nov 2023 • Vandan Mujadia, Ashok Urlana, Yash Bhaskar, Penumalla Aditya Pavani, Kukkapalli Shravya, Parameswari Krishnamurthy, Dipti Misra Sharma
In this work, our aim is to explore the multilingual capabilities of large language models by using machine translation as a task involving English and 22 Indian languages.
1 code implementation • 15 May 2023 • Ashok Urlana, Pinzhen Chen, Zheng Zhao, Shay B. Cohen, Manish Shrivastava, Barry Haddow
This paper introduces PMIndiaSum, a multilingual and massively parallel summarization corpus focused on languages in India.
1 code implementation • 25 Mar 2023 • Ashok Urlana, Sahil Manoj Bhatt, Nirmal Surange, Manish Shrivastava
This paper also extensively analyzes the impact of k-fold cross-validation while experimenting with limited data size, and we also perform various experiments with a combination of the original and a filtered version of the data to determine the efficacy of the pretrained models.