Generating clickbait spoilers with an ensemble of large language models (2023)

Abstract: Clickbait posts are a widespread problem in the webspace. The generation of spoilers, i.e. short texts that neutralize clickbait by providing information that makes it uninteresting, is one of the proposed solutions to the problem. Current state-of-the-art methods are based on passage retrieval or question answering approaches and are limited to generating spoilers only in the form of a phrase or a passage. In this work, we propose an ensemble of fine-tuned large language models for clickbait spoiler generation. Our approach is not limited to phrase or passage spoilers, but is also able to generate multipart spoilers that refer to several non-consecutive parts of text. Experimental evaluation demonstrates that the proposed ensemble model outperforms the baselines in terms of BLEU, METEOR and BERTScore metrics.

## introduction and related works catchy headlines or social media posts designed to entice users to click, known as clickbait, are widespread on the internet. although they often increase website traffic and generate revenue, they usually fall short of readers' expectations, wasting their time and causing disappointment @xcite . to deal with this problem, @xcite proposed the clickbait detection task, which received some research attention @xcite . more recently, @xcite found that clickbaits can be neutralized by providing short texts that clarify what the reader can expect from the linked article, often making the clickbait uninteresting. they developed corpora of clickbait spoilers and classified them into three types: phrase spoilers (containing a single word or a short phrase), passage spoilers (a few sentences at most), and multi-part spoilers in this work, we demonstrate that all three types of spoilers can be effectively generated by means of conditional language generation with large language models. we put forward a simple yet effective proposal of an ensemble of llms that selects the final spoiler by exploiting learning-to-rank techniques. finally, we verify the performance of the proposed approach and investigate the possibility of combining it with previously developed methods that provide phrase and passage spoilers. ## ensemble of llms for clickbait spoiler generation the task of clickbait spoiler generation is defined as follows. for a given clickbait text c, the content of the linked article a and the requested spoiler type t, generate a textual spoiler s whose goal is to make the clickbait c uninteresting for the user by providing the additional information from the referred article a. the possible types of spoilers t are phrase, passage, and multi-part. in this paper, we propose an ensemble of language models for clickbait spoiler generation. the proposed approach consists of three steps: converting the text of a clickbait c into a question q, generating candidate spoilers from various prompted large language models, and finally selecting the final spoiler by a trained scoring model. ## experiments we have performed computational experiments aimed at verifying the effectiveness of ensembling llms with pointwise and pairwise rankers, and comparing its effectiveness with the previous sota methods based on question answering. in addition, we also investigate the possibility of combining the previous qa approaches, which are best suited for passage and phrase spoiler types, with the proposed approach for multi-part spoilers. ## summary in this paper, we have shown that using fine-tuned llms can be a simple, yet effective way of dealing with clickbait spoiler generation for all three spoiler types considered, i.e. including multi-part spoilers. we also demonstrated that ensembling several such models with a ranker that selects the most suitable spoiler leads to improved results, es-pecially when using a pointwise ranker. finally, we show that combining state-of-the-art approaches for phrase and passage spoiler types based on question answering with the newly proposed ones based on llms leads to further improvements.