The creation of a quality summarization dataset is an expensive, time-consuming effort, requiring the production and evaluation of summaries by both trained humans and machines. The returns to such an effort would increase significantly if the dataset could be used in additional languages without repeating human annotations. To investigate how much we can trust machine translation of summarization datasets, we translate the English SummEval dataset to seven languages and compare performances across automatic evaluation measures. We explore equivalence testing as the appropriate statistical paradigm for evaluating correlations between human and automated scoring of summaries. We also consider the effect of translation on the relative performance between measures. We find some potential for dataset reuse in languages similar to the source and along particular dimensions of summary quality. Our code and data can be found at https://github.com/PrimerAI/primer-research/.

Only a small portion of research papers with human evaluation for text summarization provide information about the participant demographics, task design, and experiment protocol. Additionally, many researchers use human evaluation as gold standard without questioning the reliability or investigating the factors that might affect the reliability of the human evaluation. As a result, there is a lack of best practices for reliable human summarization evaluation grounded by empirical evidence. To investigate human evaluation reliability, we conduct a series of human evaluation experiments, provide an overview of participant demographics, task design, experimental set-up and compare the results from different experiments. Based on our empirical analysis, we provide guidelines to ensure the reliability of expert and non-expert evaluations, and we determine the factors that might affect the reliability of the human evaluation.

In recent years, crowdsourcing has gained much attention from researchers to generate data for the Natural Language Generation (NLG) tools or to evaluate them. However, the quality of crowdsourced data has been questioned repeatedly because of the complexity of NLG tasks and crowd workers’ unknown skills. Moreover, crowdsourcing can also be costly and often not feasible for large-scale data generation or evaluation. To overcome these challenges and leverage the complementary strengths of humans and machine tools, we propose a hybrid human-machine workflow designed explicitly for NLG tasks with real-time quality control mechanisms under budget constraints. This hybrid methodology is a powerful tool for achieving high-quality data while preserving efficiency. By combining human and machine intelligence, the proposed workflow decides dynamically on the next step based on the data from previous steps and given constraints. Our goal is to provide not only the theoretical foundations of the hybrid workflow but also to provide its implementation as open-source in future work.

The intrinsic and extrinsic quality evaluation is an essential part of the summary evaluation methodology usually conducted in a traditional controlled laboratory environment. However, processing large text corpora using these methods reveals expensive from both the organizational and the financial perspective. For the first time, and as a fast, scalable, and cost-effective alternative, we propose micro-task crowdsourcing to evaluate both the intrinsic and extrinsic quality of query-based extractive text summaries. To investigate the appropriateness of crowdsourcing for this task, we conduct intensive comparative crowdsourcing and laboratory experiments, evaluating nine extrinsic and intrinsic quality measures on 5-point MOS scales. Correlating results of crowd and laboratory ratings reveals high applicability of crowdsourcing for the factors overall quality, grammaticality, non-redundancy, referential clarity, focus, structure & coherence, summary usefulness, and summary informativeness. Further, we investigate the effect of the number of repetitions of assessments on the robustness of mean opinion score of crowd ratings, measured against the increase of correlation coefficients between crowd and laboratory. Our results suggest that the optimal number of repetitions in crowdsourcing setups, in which any additional repetitions do no longer cause an adequate increase of overall correlation coefficients, lies between seven and nine for intrinsic and extrinsic quality factors.

One of the main challenges in the development of summarization tools is summarization quality evaluation. On the one hand, the human assessment of summarization quality conducted by linguistic experts is slow, expensive, and still not a standardized procedure. On the other hand, the automatic assessment metrics are reported not to correlate high enough with human quality ratings. As a solution, we propose crowdsourcing as a fast, scalable, and cost-effective alternative to expert evaluations to assess the intrinsic and extrinsic quality of summarization by comparing crowd ratings with expert ratings and automatic metrics such as ROUGE, BLEU, or BertScore on a German summarization data set. Our results provide a basis for best practices for crowd-based summarization evaluation regarding major influential factors such as the best annotation aggregation method, the influence of readability and reading effort on summarization evaluation, and the optimal number of crowd workers to achieve comparable results to experts, especially when determining factors such as overall quality, grammaticality, referential clarity, focus, structure & coherence, summary usefulness, and summary informativeness.