Automatic text summarization of konkani texts using pre-trained word embeddings and deep learning

Abstract

<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>

Indonesian News Text Summarization Using MBART Algorithm

“…This is done in order to ensure that the split text corresponds to the same way in the corpus of the pre-trained model and also uses the same vocabulary at pre-training time. Tokenization in mBART50 is also based on SentenPiece [31]. SentecePiece can perform sub-word model training directly from raw sentences [32].…”

Sentiment Analysis of Text Reviews Using Lexicon-Enhanced Bert Embedding (LeBERT) Model with Convolutional Neural Network

“…Liu et al [36] used pre trained Word2Vec model and improved them for cross-domain classification by extending the vector to include domain information. Recently D'Silva and Sharma [37] used FastText pre-trained word embeddings and neural networks to classify Konkani texts. Hu et al [38] used BERT to integrate mental features and short text vector to improve topic classification and false detection in short text.…”