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Found 7 papers, 7 papers with code
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.
LTRC @MuP 2022: Multi-Perspective Scientific Document Summarization Using Pre-trained Generation Models
The MuP-2022 shared task focuses on multiperspective scientific document summarization.
TeClass: A Human-Annotated Relevance-based Headline Classification and Generation Dataset for Telugu
We further demonstrate the impact of this work by fine-tuning various headline generation models using TeClass dataset.
SemEval-2024 Task 1: Semantic Textual Relatedness for African and Asian Languages
We present the first shared task on Semantic Textual Relatedness (STR).
SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 14 Languages
Exploring and quantifying semantic relatedness is central to representing language.
Mukhyansh: A Headline Generation Dataset for Indic Languages
The task of headline generation within the realm of Natural Language Processing (NLP) holds immense significance, as it strives to distill the true essence of textual content into concise and attention-grabbing summaries.
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.
