Browsing by Author "ABBAS, HANAN WAHHAB"
Now showing 1 - 1 of 1
Results Per Page
Sort Options
Item ABSTRACTIVE TEXT SUMMARIZATION USING DEEP LEARNING(2022-01-11) ABBAS, HANAN WAHHAB; YILDIZ, BeytullahThe ability to produce summaries automatically helps to improve knowledge dissemination and retention, as well as efficiency in a variety of fields.There are basically two approaches to summarizing, abstractive and extractive. The abstractive approach is considered more successful as it is the process of creating a brief summary of the source text to capture the main ideas. In this approach, summaries created from the source text may contain new phrases and sentences not included in the original text. The use of attention-based Recurrent Neural Networks encoder-decoder models has been popular for a variety of language-related tasks, including summarization and machine translation. Recently, in the field of machine translation, the Transformer model has proven to be superior to the Recurrent Neural Networks-based model. In this thesis, we propose an improved encoder-decoder Transformer model for text summarization. As a baseline model, we used Long Short-Term Memory with attention, a Recurrent Neural Networks model, for the abstractive text summarization task. Evaluation of this study is performed automatically using the ROUGE score. Experimental results show that the Transformer model provides a better summary and a higher ROUGE score.