Text Summarization Corpus Generation in Low-Resource South Asian Gujarati Language

The scarcity of Datasets/Corpus belonging to resource-poor languages is a widely discussed topic. Due to the absence of widespread usage of the local language in Online platforms, the English language is dominant. Much research comes to a standstill without a proper dataset. Due to the need for more appropriate datasets in the Gujarati language, we have made a Gold standard dataset for multiple uses, mainly for Text summarization, with 76% Cohen's Kappa Inter-Annotator agreement, which is a satisfactory level to approve the dataset for the presented hypothesis. Also, the TF - IDF score for the baseline system can be helpful in further research. We have also measured the formality score of the annotator's text. For annotator-1, it received an 82.38 % formality score, and for annotator-2, it received an 81.79 % formality score, meaning annotators selected more formal statements and conveyed more information. We also checked the formality score result with the Wilcoxon Signed-Rank Test with a p-value of 0.438, which is quite acceptable.