Natalya Avanesova, Head of NLP at Preste. She has more than 5 years of practical experience in the field of NLP products development. Having PhD in Linguistics, Natalya is also the lecturer at Natural Language Processing courses, author of scientific publications and speaker at AI conferences. PRESTE is an AI service company. We carry out your Machine Learning & Data Science projects – from design to production. We support you in computer vision, natural language processing (NLP), predictive analytics, recommender systems, graphs and tailored algorithms.
Automatic Text summarization: is GPT3 the best and only?
Data Science and Machine Learning
Session Language |Ukraine
Automatic text summarization is still one of the most challenging and interesting problems in the field of Natural Language Processing. The demand for automatic text summarization systems is spiking these days thanks to the availability of large amounts of textual data, and it really can aid many downstream applications such as creating news digests, report generation, users opinion summarization and so on. Sophisticated abilities that are crucial to high-quality summarization are possible only in an abstractive framework. Meanwhile, GPT-3 could help to generate human-like summaries of desired quality. But is it really the best and the only way to generate a high-quality summary? In speech, we will explore the realms of abstractive summarization using GPT3 and alternative techniques and we will compare them according to their performance for achieving the required summary.