The surge in text data has driven extensive research into developing diverse automatic
summarization approaches to effectively handle vast textual information. There are several …

While abstractive summarization in certain languages, like English, has already reached
fairly good results due to the availability of trend-setting resources, like the CNN/Daily Mail …

Sentiment analysis is a powerful tool to gain insight into the emotional polarity of
opinionated texts. Computerized applications can contribute to the establishment of …

We publish an abstractive summarizer for Hungarian, an encoder-decoder model initialized
with huBERT, and fine-tuned on the ELTE. DH corpus of former Hungarian news portals …

We introduce HunSum-1: a dataset for Hungarian abstractive summarization, consisting of
1.14 M news articles. The dataset is built by collecting, cleaning and deduplicating data from …

One of the most important NLP tasks for the industry today is to produce an extract from
longer text documents. This task is one of the hottest topics for the researchers and they …

Training summarization models requires substantial amounts of training data. However for
less resourceful languages like Hungarian, openly available models and datasets are …

The main task of our research is to train various abstractive summarization models for the
Arabic language. The work for abstractive Arabic text summarization has hardly begun so far …

In recent years, natural language processing tasks, like sentiment analysis, can be solved
with high performance techniques, if a pre-trained language model is fine-tuned. However …

In the rapidly evolving field of sentiment analysis, the IMDB movie review dataset has
become one of the key benchmarks for evaluating the performance of machine learning …