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.

Massive knowledge graphs like Wikidata attempt to capture world knowledge about multiple entities. Recent approaches concentrate on automatically enriching these KGs from text. However a lot of information present in the form of natural text in low resource languages is often missed out. Cross Lingual Information Extraction aims at extracting factual information in the form of English triples from low resource Indian Language text. Despite its massive potential, progress made on this task is lagging when compared to Monolingual Information Extraction. In this paper, we propose the task of Cross Lingual Fact Extraction(CLFE) from text and devise an end-to-end generative approach for the same which achieves an overall F1 score of 77.46