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1. The CNN-Corpus in Spanish: a Large Corpus for Extractive Text Summarization in the Spanish Language

   https://doi.org/10.1145/3342558.3345423  

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

   This paper details the development and features of the CNN-corpus in Spanish, possibly the largest test corpus for single document extractive text summarization in the Spanish language. Its current version encompasses 1,117 well-written texts in Spanish, each of them has an abstractive and an extractive summary. The development methodology adopted allows good-quality qualitative and quantitative assessments of summarization strategies for tools developed in the Spanish language. 

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2. 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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3. 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. 

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4. SUMDocS: Surrounding-aware Unsupervised Multi-Document Summarization

   https://doi.org/10.1137/1.9781611976700.54  

   Qi Zhu, Fang Guo (April 2021, Proceedings of the 2021 {SIAM} International Conference on Data Mining, {SDM} 2021)

   Carlotta Demeniconi, Ian Davidson: (Ed.)

   Multi-document summarization, which summarizes a set of documents with a small number of phrases or sentences, provides a concise and critical essence of the documents. Existing multi-document summarization methods ignore the fact that there often exist many relevant documents that provide surrounding background knowledge, which can help generate a salient and discriminative summary for a given set of documents. In this paper, we propose a novel method, SUMDocS (Surrounding-aware Unsupervised Multi-Document Summarization), which incorporates rich surrounding (topically related) documents to help improve the quality of extractive summarization without human supervision. Speci fically, we propose a joint optimization algorithm to unify global novelty (i.e., category-level frequent and discriminative), local consistency (i.e., locally frequent, co-occurring), and local saliency (i.e., salient from its surroundings) such that the obtained summary captures the characteristics of the target documents. Extensive experiments on news and scientifi c domains demonstrate the superior performance of our method when the unlabeled surrounding corpus is utilized. 

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5. Multiplex Graph Neural Network for Extractive Text Summarization

   https://doi.org/10.18653/v1/2021.emnlp-main.11  

   Jing, Baoyu; You, Zeyu; Yang, Tao; Fan, Wei; Tong, Hanghang (January 2021, EMNLP)

   Extractive text summarization aims at extract- ing the most representative sentences from a given document as its summary. To extract a good summary from a long text document, sen- tence embedding plays an important role. Re- cent studies have leveraged graph neural net- works to capture the inter-sentential relation- ship (e.g., the discourse graph) to learn con- textual sentence embedding. However, those approaches neither consider multiple types of inter-sentential relationships (e.g., semantic similarity & natural connection), nor model intra-sentential relationships (e.g, semantic & syntactic relationship among words). To ad- dress these problems, we propose a novel Mul- tiplex Graph Convolutional Network (Multi- GCN) to jointly model different types of rela- tionships among sentences and words. Based on Multi-GCN, we propose a Multiplex Graph Summarization (Multi-GraS) model for extrac- tive text summarization. Finally, we evaluate the proposed models on the CNN/DailyMail benchmark dataset to demonstrate the effec- tiveness of our method. 

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