Amin Omidvar

Headlines play a crucial role in summarizing news articles and attracting readers to the articles. Headline generation is a type of text summarization that can employ either an extractive or abstractive approach, with the latter being more prevalent through deep learning models. However, creating a popular headline that captures readers’ attention is challenging. To address this issue, we propose a hybrid headline generation approach that utilizes state-of-the-art transformer models to generate various headline variations for an article, and a headline popularity prediction model to select the most popular one. We also create a new dataset for headline popularity prediction by scraping news media’s Twitter accounts. Our evaluation demonstrates how fine-tuning summarization models for the headline generation task can significantly improve their performance for headline generation. We also show that our proposed method can generate popular headlines better than then baseline methods that do not incorporate popularity prediction. For such an evaluation purpose, we create a popularity benchmark to automatically assess the effectiveness of our proposed headline generation approach in generating popular headlines.

Lixian Liu, Amin Omidvar, Zongyang Ma, Ameeta Agrawal and Aijun An. Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing. 2022.

Online news media sometimes use misleading headlines to lure users to open the news article. These catchy headlines that attract users but disappointed them at the end, are called Clickbaits. Because of the importance of automatic clickbait detection in online medias, lots of machine learning methods were proposed and employed to find the clickbait headlines. In this research, a model using deep learning methods is proposed to find the clickbaits in Clickbait Challenge 2017's dataset. The proposed model gained the first rank in the Clickbait Challenge 2017 in terms of Mean Squared Error. Also, data analytics and visualization techniques are employed to explore and discover the provided dataset to get more insight from the data.

Credit scoring has become a very important issue due to the recent growth of the credit industry. As the first objective, this chapter provides an academic database of literature between and proposes a classification scheme to classify the articles. The second objective of this chapter is to suggest the employing of the Optimally Weighted Fuzzy K-Nearest Neighbor (OWFKNN) algorithm for credit scoring. To show the performance of this method, two real world datasets from UCI database are used. In classification task, the empirical results demonstrate that the OWFKNN outperforms the conventional KNN and fuzzy KNN methods and also other methods. In the predictive accuracy of probability of default, the OWFKNN also show the best performance among the other methods. The results in this chapter suggest that the OWFKNN approach is mostly effective in estimating default probabilities and is a promising method to the fields of classification.