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Found 10 papers, 5 papers with code
LTRC @MuP 2022: Multi-Perspective Scientific Document Summarization Using Pre-trained Generation Models
The MuP-2022 shared task focuses on multiperspective scientific document summarization.
TeSum: Human-Generated Abstractive Summarization Corpus for Telugu
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.
TrustAI at SemEval-2024 Task 8: A Comprehensive Analysis of Multi-domain Machine Generated Text Detection Techniques
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.
LimGen: Probing the LLMs for Generating Suggestive Limitations of Research Papers
Examining limitations is a crucial step in the scholarly research reviewing process, revealing aspects where a study might lack decisiveness or require enhancement.
LLMs with Industrial Lens: Deciphering the Challenges and Prospects -- A Survey
Large language models (LLMs) have become the secret ingredient driving numerous industrial applications, showcasing their remarkable versatility across a diverse spectrum of tasks.
Automatic Data Retrieval for Cross Lingual Summarization
We analyze the data and propose methods to match articles to video descriptions that serve as document and summary pairs.
Assessing Translation capabilities of Large Language Models involving English and Indian Languages
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.
Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects -- A Survey
Generic text summarization approaches often fail to address the specific intent and needs of individual users.
PMIndiaSum: Multilingual and Cross-lingual Headline Summarization for Languages in India
This paper introduces PMIndiaSum, a multilingual and massively parallel summarization corpus focused on languages in India.
Indian Language Summarization using Pretrained Sequence-to-Sequence Models
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.
