ML Mob at S em E val-2023 Task 5: “Breaking News: Our Semi-Supervised and Multi-Task Learning Approach Spoils Clickbait” (2023)

Abstract: Online articles using striking headlines that promise intriguing information are often used to attract readers. Most of the time, the information provided in the text is disappointing to the reader after the headline promised exciting news. As part of the SemEval-2023 challenge, we propose a system to generate a spoiler for these headlines. The spoiler provides the information promised by the headline and eliminates the need to read the full article. We consider Multi-Task Learning and generating more data using a distillation approach in our system. With this, we achieve an F1 score up to 51.48% on extracting the spoiler from the articles.

## introduction the modern web is flooded with clickbait headlines and articles -text snippets that try to lure the reader into clicking a link @xcite -so readers often fall prey to misleading information. clickbait spoiling is a pressing issue that refers to the phenomenon of clickbait headlines that are used to gain attention and clicks to an article. the answer to the question or statement raised in the headline is typically a small part of the article. clickbait spoiling, the process of getting the answer (the spoiler) to the raised problem in the article, provides this small part of the article which enables readers to get the relevant information without having to go through the entire article. this work is part of the semeval 2023 challenge where our team chose to focus on the topic of clickbait spoiling @xcite , as it is a widespread problem that affects millions of internet users every day. the first of two tasks is about spoiler type classification, where an input text is given and the model classifies the spoiler as either "phrase", "passage" or "multi". a phrase spoiler denotes an article, whose headline can be answered with a single word * * equal contribution or short phrase. for "passage" spoilers the answer is a few sentences long. answers with multiple spans are classified as the "multi" spoiler type. the second task focuses on spoiler generation. a clickbait post and a linked document are used as input to generate a spoiler @xcite . within our work, we adopted the proposed baselines by @xcite . specifically, we applied multi-task learning and extended the training dataset by incorporating additional data. ## background this section introduces relevant background for the distillation approach and multi-task learning. ## system overview we propose a semi-supervised setup, allowing us to use other clickbait datasets that are not annotated with a spoiler. this enables us to use more data for training. to combine the training of both tasks we explore a multi-task setup to train both sub-tasks jointly. the system is evaluated using the tira platform @xcite . ## experimental setup in the following, we describe the experimental setup for both of our system variations. ## results looking at the mtl setup leads to an accuracy of 67.39% in the development for the spoiler classification task ( for both tasks, our models perform best for the samples with the phrase spoiler type. one possible explanation for this is the usage of models that were already fine-tuned on the squad dataset. the squad ## conclusion in this work, we present a distillation approach improving the performance of a fine-tuned deep learning model on a clickbait spoiling task. we extended the given dataset with additional automatically labeled examples through a semi-supervised learning approach from a different dataset, which led to an improvement in the model's performance. additionally, we explored a multi-task learning setup, where we tried to improve the performance on both tasks further. as our results indicate a lack in performance for longer spoiler extractions sequences, we suggest for future work to further investigate finding ways to extract these sequences.