Billy-Batson at S em E val-2023 Task 5: An Information Condensation based System for Clickbait Spoiling (2023)

Abstract: The Clickbait Challenge targets spoiling the clickbaits using short pieces of information known as spoilers to satisfy the curiosity induced by a clickbait post. The large context of the article associated with the clickbait and differences in the spoiler forms, make the task challenging. Hence, to tackle the large context, we propose an Information Condensation-based approach, which prunes down the unnecessary context. Given an article, our filtering module optimised with a contrastive learning objective first selects the parapraphs that are the most relevant to the corresponding clickbait.The resulting condensed article is then fed to the two downstream tasks of spoiler type classification and spoiler generation. We demonstrate and analyze the gains from this approach on both the tasks. Overall, we win the task of spoiler type classification and achieve competitive results on spoiler generation.

## introduction clickbait is an umbrella word for various strategies used to capture attention and stimulate people's interest @xcite . instead of offering comprehensive summaries, clickbait postings link to web pages and sell their content by raising curiosity, making them a potential tool for spreading adversities like fake news @xcite . the clickbait spoiling shared task, which is based on the english language, attempts to categorize and generate short paragraphs that fulfill the interest sparked by a clickbait @xcite through a spoiler. the task is divided into two subtasks -(i) spoiler type classification, in which we need to "identify the type" of spoiler that would be needed for the corresponding clickbait and the article, and (ii) spoiler generation, which seeks to "generate the spoiler" for a given clickbait. the second task also uses the spoiler type information along with * both the authors have equal contribution to this work. the post and article for generating the spoiler, considering the large difference in the nature of spoilers per type. although we look at both the tasks individually in this work, the overarching goal lies in having a pipeline that will first predict the type of spoiler before proceeding for spoiler generation. the task deals with three spoiler types: phrase, passage and multipart, the differences in which will be discussed in section 3. figure 1 illustrates the two tasks where the clickbait and the article warrants a spoiler of the type "passage". the dataset for both the tasks is a collection of manually spoiled clickbaits extracted from various social media platforms like facebook, reddit, and twitter @xcite . each data point consists of clickbait, multiple paragraphs in the article (which hold the content regarding the clickbait), and spoiler information. ## related work the task of spoiler detection and generation was introduced in @xcite . research studies related to clickbait have been categorised into clickbait detection and clickbait generation. the domain of automated clickbait detection, which was started by @xcite , and further independently explored by @xcite and @xcite has been heavily dominated by the transformer models. some other progress in terms of clickbait generation was done by @xcite . the task of spoiling a clickbait can be analogous to a question answering paradigm @xcite , where given a clickbait as a question, the answer would be a spoiler. ## data description for majority of our experiments, we have used the dual set up of the original webis clickbait spoiling (wcs) corpus 2022 @xcite and the paragraph-wise (pw) dataset which we derive from the wcs corpus. the wcs corpus has 14 fields giving information about the clickbait, article title, article content, spoiler category type and spoiler positions. the three types of spoilers vary on the basis of their length and structure: (1) phrase is a short span, spanning an average of 2.8 words, (2) passage is a longer span, with an average of 24.1 words and (3) multipart is a bullet-list style structure consisting of individual phrase and passage-type spoilers. dataset name train dev wcs corpus 3200 800 pw dataset 48,626 12,410 ## methodology in this section, we discuss our proposed approach consisting of paragraph-wise filtering for information condensation followed by finetuning on downstream tasks. given a clickbaity post c and an article a consisting of the page title p 0 followed by n paragraphs p 1 , p 2 , ..., p n , we need to (1) identify the spoiler type t needed to spoil the clickbait, and (2) generate a spoiler s, using the additional information of spoiler type t. ## results we discuss the quantitative results on all our primary experiments in this section. ## conclusion clickbait spoiling, as opposed to clickbait detection, which frequently includes filtering out clickbait postings from users' timelines, subverts the enthusiasm aroused by clickbait by revealing the inner details in advance. our idea of information condensation -providing a precise spoiler containing fragments of an article instead of the entire article, worked well on the spoiler type classification task and spoiler generation task on phrase type spoilers. we tried multiple techniques for information condensation, but found contrastive learning based approach to be the most optimum and used it to filter paragraphs for both the tasks. for spoiler generation of passage type, the contrastive learning approach didn't result in an improvement due to loss of spoiler containing paragraphs and issues with textual coherence. future work can look at the specific issue of spoiler containing paragraphs and would aim at reducing this loss (as results from oracle experiments validates the correctness of approach). further we can expand our experiments to generate multipart type of spoilers and analyse our findings. tianyi zhang, varsha kishore, felix wu, kilian q. weinberger, and yoav artzi. 2019. bertscore: evaluating text generation with bert. arxiv, abs/1904.09675.