Jack-flood at S em E val-2023 Task 5:Hierarchical Encoding and Reciprocal Rank Fusion-Based System for Spoiler Classification and Generation (2023)

Abstract: The rise of social media has exponentially witnessed the use of clickbait posts that grab users’ attention. Although work has been done to detect clickbait posts, this is the first task focused on generating appropriate spoilers for these potential clickbaits. This paper presents our approach in this direction. We use different encoding techniques that capture the context of the post text and the target paragraph. We propose hierarchical encoding with count and document length feature-based model for spoiler type classification which uses Recurrence over Pretrained Encoding. We also propose combining multiple ranking with reciprocal rank fusion for passage spoiler retrieval and question-answering approach for phrase spoiler retrieval. For multipart spoiler retrieval, we combine the above two spoiler retrieval methods. Experimental results over the benchmark suggest that our proposed spoiler retrieval methods are able to retrieve spoilers that are semantically very close to the ground truth spoilers.

## introduction the rapid adoption of social media among the masses has pushed online publishers to stay relevant by making their content prominent on these platforms @xcite . this is mostly achieved through clickbait. this paper presents our proposed approach and detailed analysis for two subtasks of the clickbait spoiling challenge (task 5) @xcite subtask 1: clickbait spoiler type classification, b) subtask 2: clickbait spoiler generation. this contest is organized in the 17th international workshop on semantic evaluation @xcite . this challenge addresses the issue of generating spoilers for clickbait. the importance of this task can be gauged from the fact that spoilers would preemptively warn the readers about the content of the clickbait posts, enabling the users to make an informed decision. this would also mitigate the spread of misinformation @xcite and the over-dependence of content publishers on sensational posts for prominence. our proposed methods for spoiler-type classification and spoiler generation rely on strength of pretrained language models such as bert @xcite , and its different variants like deberta @xcite . we propose a hierarchical encoding-based model for spoiler-type classifications (subtask-1), which encodes different aspects of hierarchical information presented in the target paragraph along with document length and count overlap-based features. considering the unique challenges and characteristics posed by passage, phrase and multipart spoiler generation, we adopt three different approaches for spoiler generation. we combine several ranking models using reciprocal rank fusion (rrf) for passage retrieval, a pretrained roberta model over question answering task for phrase retrieval and combine methods of passage retrieval and phrase retrieval for multipart spoiler retrieval. our code repository is available at foot_0 https://github.com/ thesujitkumar/jack-flood-at-semeval-2023-task-5_ spoiler-classification-and-generation to recreate and reproduce the results presented in this paper. ## background initial studies @xcite @xcite @xcite @xcite in the literature focus on the detection of clickbait, which deals with identifying whether a post is clickbait or not. but such detection of clickbait does not impede the curiosity induced by clickbait. to fill this gap and overcome such limitations, the recent study @xcite curated a dataset for spoiler generation and proposed information retrieval and question-answering methods for spoiler generation, which helps to impede the curiosity induced by clickbait. task 5 of semeval 2023, clickbait spoiling @xcite aims to generate spoilers for clickbait posts. generating effective spoilers poses unique challenges, such as understanding the context of the title and its relevance with the post body. we propose novel approaches to solving this problem by harnessing the power of pretrained language models and the reciprocal rank fusion technique to generate phrase, passage, and multipart-type spoilers. ## methodology this section presents our proposed method for subtask 1: spoiler type classification, and subtask 2: spoiler generation. ## experimental setup ## results and discussion considering the bsc. score of systems from ## conclusion we propose a hierarchical encoding-based model recurrence over pretrained encoding rp e for spoiler-type classification. this paper adopts reciprocal rank fusion (rrf) for passage retrieval and adopt question answering based approach for phrase spoiler retrieval, and combine the passage and phrase retrieval model for multipart spoiler retrieval.our results suggest that the proposed system effectively identifies spoiler types and retrieves spoilers from the target paragraph, which are semantically very close to the ground truth spoiler. in future work, we plan to explore extractive summarization methods for spoiler generation. tianyi zhang, varsha kishore, felix wu, kilian q weinberger, and yoav artzi. 2019. bertscore: evaluating text generation with bert. arxiv preprint arxiv:1904.09675.