SPICED: News Similarity Detection Dataset with Multiple Topics and Complexity Levels
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Abstract (arXiv)
The proliferation of news media outlets has increased the demand for intelligent systems capable of detecting redundant information in news articles in order to enhance user experience. However, the heterogeneous nature of news can lead to spurious findings in these systems: Simple heuristics such as whether a pair of news are both about politics can provide strong but deceptive downstream performance. Segmenting news similarity datasets into topics improves the training of these models by forcing them to learn how to distinguish salient characteristics under more narrow domains. However, this requires the existence of topic-specific datasets, which are currently lacking. In this article, we propose a novel dataset of similar news, SPICED, which includes seven topics: Crime & Law, Culture & Entertainment, Disasters & Accidents, Economy & Business, Politics & Conflicts, Science & Technology, and Sports. Futhermore, we present four different levels of complexity, specifically designed for news similarity detection task. We benchmarked the created datasets using MinHash, BERT, SBERT, and SimCSE models.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 16 of its 54 references that are in the map. First posted on arXiv 21 Sep 2023.
Builds on
16 of 54 references.
References · 16
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
- 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,811
- 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,173
- 2017SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluationcited by 1,001
- 2018SentEval: An Evaluation Toolkit for Universal Sentence Representationscited by 294
- 2015Yara Parser: A Fast and Accurate Dependency Parsercited by 111
- 2021ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embeddingcited by 74
- 2020ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corporacited by 63
- 2018Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learningcited by 52
4 more
- 2018MedSTS: A Resource for Clinical Semantic Textual Similaritycited by 39
- 2019Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understandingcited by 37
- 2020CORD19STS: COVID-19 Semantic Textual Similarity Datasetcited by 7
- 2021Automated Query Reformulation for Efficient Search based on Query Logs From Stack Overflowcited by 4
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.