Abstract
AbstractDuring the last two decades, automatic evaluation has been significant for NLP tasks because it is necessary to measure the scope of NLP systems in terms of robustness and efficacy. In the context of Automatic Text Summarization (ATS), proposed methods are evaluated depending on how well they can produce informative, coherent, readable, responsive, and non-redundant summaries. Therefore, such methods are focused on linguistic and content analysis of summaries. This chapter explores the different state-of-the-art evaluation methods that evaluate summaries with or without human references. Moreover, it describes two novel alternatives related to the optimization of content metrics to improve automatic evaluation.
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