More Like this

1. The CNN-Corpus: A Large textual Corpus for Single-Document Extractive Summarization

   https://doi.org/10.1145/3342558.3345388  

   Lins, Rafael Dueire; Oliveira, Hilario; Cabral, Luciano; Batista, Jamilson; Tenorio, Bruno; Ferreira, Rafael; Lima, Rinaldo; de França Pereira e Silva, Gabriel; Simske, Steven J (October 2019, Proceedings of the ACM Symposium on Document Engineering)

   This paper details the features and the methodology adopted in the construction of the CNN-corpus, a test corpus for single document extractive text summarization of news articles. The current version of the CNN-corpus encompasses 3,000 texts in English, and each of them has an abstractive and an extractive summary. The corpus allows quantitative and qualitative assessments of extractive summarization strategies. 

   more » « less
2. The Multilingual Amazon Reviews Corpus

   https://doi.org/10.18653/v1/2020.emnlp-main.369  

   Keung, Phillip; Lu, Yichao; Szarvas, György Szarvas; Smith, Noah A. (January 2020, Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP))

   We present the Multilingual Amazon Reviews Corpus (MARC), a large-scale collection of Amazon reviews for multilingual text classification. The corpus contains reviews in English, Japanese, German, French, Spanish, and Chinese, which were collected between 2015 and 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID, and the coarse-grained product category (e.g., ‘books’, ‘appliances’, etc.) The corpus is balanced across the 5 possible star ratings, so each rating constitutes 20% of the reviews in each language. For each language, there are 200,000, 5,000, and 5,000 reviews in the training, development, and test sets, respectively. We report baseline results for supervised text classification and zero-shot cross-lingual transfer learning by fine-tuning a multilingual BERT model on reviews data. We propose the use of mean absolute error (MAE) instead of classification accuracy for this task, since MAE accounts for the ordinal nature of the ratings. 

   more » « less
3. ConfliBERT-Spanish: A Pre-trained Spanish Language Model for Political Conflict and Violence

   https://doi.org/10.1109/CiSt56084.2023.10409883  

   Yang, Wooseong; Alsarra, Sultan; Abdeljaber, Luay; Zawad, Niamat; Delaram, Zeinab; Osorio, Javier; Khan, Latifur; Brandt, Patrick T; D’Orazio, Vito (December 2023, Vakka)

   This article introduces ConfliBERT-Spanish, a pre-trained language model specialized in political conflict and violence for text written in the Spanish language. Our methodology relies on a large corpus specialized in politics and violence to extend the capacity of pre-trained models capable of processing text in Spanish. We assess the performance of ConfliBERT-Spanish in comparison to Multilingual BERT and BETO baselines for binary classification, multi-label classification, and named entity recognition. Results show that ConfliBERT-Spanish consistently outperforms baseline models across all tasks. These results show that our domain-specific language-specific cyberinfrastructure can greatly enhance the performance of NLP models for Latin American conflict analysis. This methodological advancement opens vast opportunities to help researchers and practitioners in the security sector to effectively analyze large amounts of information with high degrees of accuracy, thus better equipping them to meet the dynamic and complex security challenges affecting the region. 

   more » « less
4. DocEng'19 Competition on Extractive Text Summarization

   https://doi.org/10.1145/3342558.3351874  

   Lins, Rafael Dueire; Mello, Rafael Ferreira; Simske, Steve (October 2019, Proceedings of the ACM Symposium on Document Engineering)

   The DocEng’19 Competition on Extractive Text Summarization assessed the performance of two new and fourteen previously published extractive text sumarization methods. The competitors were evaluated using the CNN-Corpus, the largest test set available today for single document extractive summarization. 

   more » « less
5. The Removal of Irrelevant Human Factors in a Multi-Review Corpus through Text Filtering

   https://doi.org/10.54941/ahfe1003766  

   Moody, Aaron; Spurling, Makenzie; Hu, Chenyi (January 2023, Accelerating Open Access Science in Human Factors Engineering and Human-Centered Computing)

   Generating a high-quality explainable summary of a multi-review corpus can help people save time in reading the reviews. With natural language processing and text clustering, people can generate both abstractive and extractive summaries on a corpus containing up to 967 product reviews (Moody et al. 2022). However, the overall quality of the summaries needs further improvement. Noticing that online reviews in the corpus come from a diverse population, we take an approach of removing irrelevant human factors through pre-processing. Apply available pre-trained models together with reference based and reference free metrics, we filter out noise in each review automatically prior to summary generation. Our computational experiments evident that one may significantly improve the overall quality of an explainable summary from such a pre-processed corpus than from the original one. It is suggested of applying available high-quality pre-trained tools to filter noises rather than start from scratch. Although this work is on the specific multi-review corpus, the methods and conclusions should be helpful for generating summaries for other multi-review corpora. 

   more » « less