UM 6 P at S em E val-2023 Task 3: News genre classification based on transformers, graph convolution networks and number of sentences (2023)

Abstract: This paper presents our proposed method for english documents genre classification in the context of SemEval 2023 task 3, subtask 1. Our method use ensemble technique to combine four distinct models predictions: Longformer, RoBERTa, GCN, and a sentences number-based model. Each model is optimized on simple objectives and easy to grasp. We provide snippets of code that define each model to make the reading experience better. Our method ranked 12th in documents genre classification for english texts.

## introduction detecting the genre, framing, and persuasion techniques in online news articles is a crucial task in media analysis, with applications ranging from content curation to political campaign analysis. with the increasing diversity of online news sources and the proliferation of multiple languages, the ability to perform such analysis in a multilingual setup is becoming ever more important. in this context, the authors @xcite proposed a new competition in the context of the semeval 2023 shared task, which aims to encourage the use of artificial intelligence (ai) in media analysis. the competition's dataset is not publicly available and covers various aspects of what makes a text persuasive, such as the genre (opinion, report, or satire), framing (which key aspects are highlighted), and rhetoric (persuasion techniques used to influence the reader). the shared task includes three subtasks: news genre categorization, framing detection, and persuasion techniques detection. the participants can choose to take part in any number of subtasklanguage pairs. the dataset is available in six languages, and participants can train their systems using the data for all languages in a multilingual setup. as part of our participation in the semeval 2023 task 3, we will be focusing on the news genre categorisation (subtask 1), working specifically in a monolingual setting with english language texts. our aim is to develop and test systems that can accurately classify news articles according to their genre. through our participation in this shared task, we hope to contribute to the growing field of ai-based media analytics. in this paper, we present our method for english document genre classification, which combines four different models to achieve promising results. our method integrates 1) longformer and roberta, two deep learning models based on transformer architecture that have demonstrated strong performance in various nlp tasks @xcite @xcite , with 2) a graph convolutional network (gcn) that captures the relationships between different parts of the input text @xcite . additionally, we incorporate a sentence numberbased model based on the count of the number of sentences in each document. finally, we use a voting mechanism to determine the most likely genre. our method scored 39.35% and ranked 12th in the english test leaderboard. ## method our method aims to build an english document genre classifier leveraging an ensemble model based on four distinct models: longformer, roberta, gcn, and a sentence number-based model. the following paragraphs describe the proposed method. ## conclusion this paper presents our method for english document genre classification in semeval 2023 task 3, which leverages machine learning and deep learning techniques. our method combines multiple predictors, including longformer, roberta, gcn, and sentence number-based model to predict the genre of an english text document. the obtained results show that our method achieved 39.35% f1 macro and ranked 12th in semeval 2023 task 3. this demonstrates the effectiveness of our method in addressing news document genre classification tasks. we hope that our approach will inspire further research and improvements in the field of document genre classification.