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Matching news articles and wikipedia tables for news augmentation

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Abstract

Nowadays, digital-news understanding is often overwhelmed by the deluge of online information. One approach to cover this gap is to outline the news story by highlighting the most relevant facts. For example, recent studies summarize news articles by generating representative headlines. In this paper, we go beyond and argue news understanding can also be enhanced by surfacing contextual data relevant to the article, such as structured web tables. Specifically, our goal is to match news articles and web tables for news augmentation. For that, we introduce a novel BERT-based attention model to compute this matching degree. Through an extensive experimental evaluation over Wikipedia tables, we compare the performance of our model with standard IR techniques, document/sentence encoders and neural IR models for this task. The overall results point out our model outperforms all baselines at different levels of accuracy and in the mean reciprocal ranking measure.

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Fig. 1
Fig. 2

Availability of data and materials

The datasets and source code are public available in: https://github.com/levysouza/News-Table-Matching.

Notes

  1. The Web has over 14.1B tables Cafarella et al. [3].

  2. https://news.google.com.

  3. https://news.microsoft.com.

  4. https://artincontext.org/most-expensive-paintings.

  5. We demonstrate its performance in our ablation study (see results in Table 8).

  6. https://github.com/levysouza/News-Table-Matching.

  7. The BERT architecture has over 340 million parameters.

  8. We also try Long Short Term Memory (LSTM) for this step but GRUs achieved better results.

  9. Our ablation study shows we can improve the model performance by joining such two attention methodologies (see results in Table 8).

  10. https://tinyurl.com/ranking-loss.

  11. We try the following similarity thresholds for the cosine distance over positive and negative pairs: 0.3, 0.4, 0.5, 0.6, and 0.7, in which 0.3 achieves the best results in our validation dataset.

  12. https://newspaper.readthedocs.io/en/latest/.

  13. https://www.elastic.co.

  14. https://fasttext.cc/.

  15. https://scikit-learn.org.

  16. https://pypi.org/project/rank-bm25.

  17. https://radimrehurek.com/gensim/auto_examples/tutorials/run_doc2vec_lee.html.

  18. https://tfhub.dev/google/universal-sentence-encoder/4.

  19. https://github.com/hanxiao/bert-as-service.

  20. https://huggingface.co/transformers/model_doc/bert.html.

  21. https://github.com/NTMC-Community/MatchZoo.

  22. https://www.sbert.net/docs/pretrained-models/dpr.html.

  23. We do not combine the table-content and its surrounding text since the table-aspects achieve the worst results for the evaluated metric.

  24. The training time for cross and bi-encoder BERT-based models are similar as both of them are composed of the BERT architecture. In our experiments, their fine-tuning time is over 20min per epoch.

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Acknowledgements

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Funding

This work is supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) from Brazil.

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  1. Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil

    Levy Silva & Luciano Barbosa

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  1. Levy Silva
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Silva, L., Barbosa, L. Matching news articles and wikipedia tables for news augmentation. Knowl Inf Syst 65, 1713–1734 (2023). https://doi.org/10.1007/s10115-022-01815-0

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