T ohoku NLP at S em E val-2023 Task 5: Clickbait Spoiling via Simple S eq2 S eq Generation and Ensembling (2023)

Abstract: This paper describes our system submitted to SemEval-2023 Task 5: Clickbait Spoiling. We work on spoiler generation of the subtask 2 and develop a system which comprises two parts: 1) simple seq2seq spoiler generation and 2) post-hoc model ensembling. Using this simple method, we address the challenge of generating multipart spoiler. In the test set, our submitted system outperformed the baseline by a large margin (approximately 10 points above on the BLEU score) for mixed types of spoilers. We also found that our system successfully handled the challenge of the multipart spoiler, confirming the effectiveness of our approach.

## introduction clickbait is a type of text on social media that is specifically designed to exploit users' curiosity and lure them into clicking on a linked webpage. due to the deceptive and misleading nature of the content often presented, clickbait is generally considered harmful @xcite . thus addressing the clickbait issue is a crucial concern for social media users. subtask 2 of semeval-2023 task 5 aims to generate spoilers of clickbait based on posted text and the linked webpage @xcite . @xcite proposed a method for generating spoilers via encoder-based question answering approach. however, their method has limitations in handling a specific type of spoiler where the answer is spread across several sections of the target article. to tackle this problem, we develop a simple yet effective sequence-to-sequence (seq2seq) approach for the subtask 2: spoiler generation. in our † equal contributions ‡ work done while at riken aip and tohoku university. 1 the docker image of our final submission on tira @xcite is registry.webis.de/code-research/ tira/tira-user-john-king/finalsub:0.0.4 and the implementation is available at https://github.com/ cl-tohoku/semeval2023-task5-seq2seq-ensemble . figure 1 : overview of semeval-2023 task 5: clickbait spoiling. the task is to extract one or more spoilers for posttext from content (targettitle and targetparagraphs). the text highlighted with yellow in the content is the spoiler to be extracted. in analysis, we show that our system generates spoilers extractively rather than abstractively, even though we adapt the seq2seq generation approach. we also examine the errors made by our system and discuss the characteristics and potential difficulty of this task. ## task description the semeval-2023 task 5 organizers offer the webis clickbait spoiling corpus 2022 dataset @xcite . this dataset collects clickbait posts on social media (facebook, reddit, and twitter), linked web pages in the posts, and various related information. the following information in the dataset is employed in this task (2023): the human-extracted clickbait spoiler for posttext either from targettitle or targetparagraphs. the task is to extract spoiler from targettitle and targetparagraphs that spoils the clickbait post of posttext (figure 1 ). spoiler to be extracted are classified into the following three categories based on their structures (2023): the provided training data consist of 3200 samples. the counts for each type in the training data are 1367 (around 43%) for phrase, 1274 (around 40%) for passage, and 559 (around 17%) for multi. the agreement between the model's predictions and the ground truths is evaluated based on the following three metrics: bleu-4 @xcite , bertscore @xcite , and meteor 1.5 @xcite , an extended version of meteor @xcite . ## system overview this section presents an overview of the proposed system. our system generates spoilers in two steps; first, multiseed seq2seq models generate spoilers, and then the generated outputs are postprocessively ensembled using a similarity-based ensemble approach. ## analysis in this section, we examine our submitted system with the model ensembling. ## discussion ## conclusion we presented our system submitted to the subtask 2 of semeval-2023 task 5: clickbait spoiling. our system comprised two parts: 1) simple seq2seq spoiler generation and 2) post-hoc model ensembling. experimental findings demonstrated that our system effectively addressed the multispan spoiler issue through seq2seq generation. additionally, our system tended to generate sentences by identifying the appropriate position in the document, even though it was not specifically trained to extract text. the analysis suggests that integrating reliable type classifiers can further improve the performance of our system. this will be future work.