most citedAutomating question generation from educational text

2 citations · 5 across the 5 of their papers we have counts for

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5 papers

cs.CL20241 cited

Leveraging the Power of LLMs: A Fine-Tuning Approach for High-Quality Aspect-Based Summarization

Ankan Mullick, Sombit Bose, Rounak Saha +6

The ever-increasing volume of digital information necessitates efficient methods for users to extract key insights from lengthy documents. Aspect-based summarization offers a targe…

cs.CY20242 cited

Building a Domain-specific Guardrail Model in Production

Mohammad Niknazar, Paul V Haley, Latha Ramanan +12

Generative AI holds the promise of enabling a range of sought-after capabilities and revolutionizing workflows in various consumer and enterprise verticals. However, putting a mode…

cs.CL2024

On The Persona-based Summarization of Domain-Specific Documents

Ankan Mullick, Sombit Bose, Rounak Saha +5

In an ever-expanding world of domain-specific knowledge, the increasing complexity of consuming, and storing information necessitates the generation of summaries from large informa…

cs.CL2024

Long Dialog Summarization: An Analysis

Ankan Mullick, Ayan Kumar Bhowmick, Raghav R +4

Dialog summarization has become increasingly important in managing and comprehending large-scale conversations across various domains. This task presents unique challenges in captu…

cs.CL20232 cited

Automating question generation from educational text

Ayan Kumar Bhowmick, Ashish Jagmohan, Aditya Vempaty +5

The use of question-based activities (QBAs) is wide-spread in education, traditionally forming an integral part of the learning and assessment process. In this paper, we design and…