H.3.8. Natural Language Processing
Davud Mohammadpur; Mehdi Nazari
Abstract
Text summarization has become one of the favorite subjects of researchers due to the rapid growth of contents. In title generation, a key aspect of text summarization, creating a concise and meaningful title is essential as it reflects the article's content, objectives, methodologies, and findings. Thus, ...
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Text summarization has become one of the favorite subjects of researchers due to the rapid growth of contents. In title generation, a key aspect of text summarization, creating a concise and meaningful title is essential as it reflects the article's content, objectives, methodologies, and findings. Thus, generating an effective title requires a thorough understanding of the article. Various methods have been proposed in text summarization to automatically generate titles, utilizing machine learning and deep learning techniques to improve results. This study aims to develop a title generation system for scientific articles using transformer-based methods to create suitable titles from article abstracts. Pre-trained transformer-based models like BERT, T5, and PEGASUS are optimized for constructing complete sentences, but their ability to generate scientific titles is limited. We have attempted to improve this limitation by presenting a proposed method that combines different models along with a suitable dataset for training. To create our desired dataset, we collected abstracts and titles of articles published on the ScienceDirect.com website. After performing preprocessing on this data, we developed a suitable dataset consisting of 50,000 articles. The results from the evaluations of the proposed method indicate more than 20% improvement based on various ROUGE metrics in the generation of scientific titles. Additionally, an examination of the results by experts in each scientific field revealed that the generated titles are also acceptable to these specialists.
M. Molaei; D. Mohamadpur
Abstract
Performing sentiment analysis on social networks big data can be helpful for various research and business projects to take useful insights from text-oriented content. In this paper, we propose a general pre-processing framework for sentiment analysis, which is devoted to adopting FastText with Recurrent ...
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Performing sentiment analysis on social networks big data can be helpful for various research and business projects to take useful insights from text-oriented content. In this paper, we propose a general pre-processing framework for sentiment analysis, which is devoted to adopting FastText with Recurrent Neural Network variants to prepare textual data efficiently. This framework consists of three different stages of data cleansing, tweets padding, word embedding’s extraction from FastText and conversion of tweets to these vectors, which implemented using DataFrame data structure in Apache Spark. Its main objective is to enhance the performance of online sentiment analysis in terms of pre-processing time and handle large scale data volume. In addition, we propose a distributed intelligent system for online social big data analytics. It is designed to store, process, and classify a huge amount of information in online. The proposed system adopts any word embedding libraries like FastText with different distributed deep learning models like LSTM or GRU. The results of the evaluations show that the proposed framework can significantly improve the performance of previous RDD-based methods in terms of processing time and data volume.
