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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. 

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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. 

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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. 

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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. 

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5. Enhancing Code-Switching Research Through Comparable Corpora: Introducing the El Paso Bilingual Corpus

   https://doi.org/10.3390/languages10070174  

   Vanhaverbeke, Margot; Enghels, Renata; Parafita_Couto, María_del Carmen; Ivanova, Iva (July 2025, Languages)

   Research on language contact outcomes, such as code-switching, continues to face theoretical and methodological challenges, particularly due to the difficulty of comparing findings across studies that use divergent data collection methods. Accordingly, scholars have emphasized the need for publicly available and comparable bilingual corpora. This paper introduces the El Paso Bilingual Corpus, a new Spanish–English bilingual corpus recorded in El Paso (TX) in 2022, designed to be methodologically comparable to the Bangor Miami Corpus. The paper is structured in three main sections. First, we review the existing Spanish–English corpora and examine the theoretical challenges posed by studies using non-comparable methodologies, thereby underscoring the gap addressed by the El Paso Bilingual Corpus. Second, we outline the corpus creation process, discussing participant recruitment, data collection, and transcription, and provide an overview of these data, including participants’ sociolinguistic profiles. Third, to demonstrate the practical value of methodologically aligned corpora, we report a comparative case study on diminutive expressions in the El Paso and Bangor Miami corpora, illustrating how shared collection protocols can elucidate the role of community-specific social factors on bilinguals’ morphosyntactic choices. 

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