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clickbait_spoiling_data/README.md

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+# Webis Clickbait Spoiling Corpus
+
+The Webis Clickbait Spoiling Corpus 2022 (Webis-Clickbait-22) contains 5,000 spoiled clickbait posts crawled from Facebook, Reddit, and Twitter.
+This corpus supports the task of clickbait spoiling, which deals with generating a short text that satisfies the curiosity induced by a clickbait post.
+
+This dataset contains the clickbait posts and manually cleaned versions of the linked documents, and extracted spoilers for each clickbait post.
+Additionally, the spoilers are categorized into three types: short phrase spoilers, longer passage spoilers, and multiple non-consecutive pieces of text. The test set of this dataset was used for the [SemEval-2023 clickbait spoiling task](https://www.tira.io/task-overview/clickbait-spoiling). You can re-execute and adopt the software submissions made through for this SemEval task, please see the instructions and overview of approaches in [TIRA](https://www.tira.io/task-overview/clickbait-spoiling).
+
+
+## Overview
+
+The dataset comes with predefined train/validation/test splits:
+
+- [3,200 posts for training](training.jsonl)
+- [800 posts for validation](validation.jsonl)
+- [1,000 posts for testing](test.jsonl)
+  - The test set was used for the [SemEval-2023 clickbait spoiling task](https://www.tira.io/task-overview/clickbait-spoiling). This shared task was organized with [TIRA.io](https://www.tira.io/) and participants submitted Docker software during the task. Please see the instructions in [TIRA](https://www.tira.io/task-overview/clickbait-spoiling) to re-execute or modify the approaches.

clickbait_spoiling_data/train.jsonl

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