Text Summarization is an emerging field of research in Natural Language Processing (NLP). A bulk of the work is related to texts in English and other popular languages. This paper presents some of the early works attempted at performing single document extractive Automatic Text Summarization on Konkani language documents, which is an under-research language in the domain of Automatic Text Summarization (ATS). The input documents need to be cleaned of punctuation and then sentence scores are calculated for each sentence in the document. The scores for each sentence are computed using Term-Frequency/Inverse Document Frequency (TF-IDF) of constituent words and overlap with the title of the story and its positional value. K-means algorithm is applied to determine clusters of sentences for the formation of the final summary. The value of 'K' is determined using the Elbow method. The dataset employed was specially designed by the authors of the paper to perform the experiments. It consists of folk tales derived from books on Konkani literature. The performance assessment of the output summaries indicated that the summaries obtained by using three clusters were better than the ones obtained using two clusters. The proposed system exhibited promising outcome, considering, no language-dependent domain knowledge or any training corpora was utilized.

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<span lang="EN-US">Automatic text summarization has gained immense popularity in research. Previously, several methods have been explored for obtaining effective text summarization outcomes. However, most of the work pertains to the most popular languages spoken in the world. Through this paper, we explore the area of extractive automatic text summarization using deep learning approach and apply it to Konkani language, which is a low-resource language as there are limited resources, such as data, tools, speakers and/or experts in Konkani. In the proposed technique, Facebook’s fastText <br /> pre-trained word embeddings are used to get a vector representation for sentences. Thereafter, deep multi-layer perceptron technique is employed, as a supervised binary classification task for auto-generating summaries using the feature vectors. Using pre-trained fastText word embeddings eliminated the requirement of a large training set and reduced training time. The system generated summaries were evaluated against the ‘gold-standard’ human generated summaries with recall-oriented understudy for gisting evaluation (ROUGE) toolkit. The results thus obtained showed that performance of the proposed system matched closely to the performance of the human annotators in generating summaries.</span>

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The aim and objective of this research are to create a model to measure the hate speech and to measure the contents of hate speech. The descriptive analysis method of data science was used to describe and summarize raw data from a dataset. We used Twitter as the social networking Web site for this research to analyze and measure the hate speech and its classifications. A dataset from kaggle datasets was applied for this research. To produce statistical results, we used monkey learn machine learning libraries which are incorporated with Python program to design and develop a model to classify and measure hate speech and its types that could be trained and tested using sentiment analysis. Researchers have found that the majority of the tweets are based on racist and ethnicity, sex and religion-based hate speech are also widely available.

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