SPICED: News Similarity Detection Dataset with Multiple Topics and Complexity Levels (2024.lrec-main)
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| Challenge: | Existing semantic textual similarity (STS) datasets are not suitable for news similarity detection due to their specificity to a single topic. |
| Approach: | They propose to segment news similarity datasets into topics to improve model training . they propose four different levels of complexity specifically designed for news similarities detection task . |
| Outcome: | The proposed dataset includes seven topics: Crime & Law, Culture & Entertainment, Disasters & Accidents, Economy & Business, Politics / Conflicts, Science & Technology, Sports. |
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