Machine translation (MT), especially Google Translate (GT), is widely used by language learners and those who need help with translation. MT research, particularly that which examines the quality and usability of the translation produced by the MT, only makes up a handful of studies. Moreover, only a few of them have looked at translation quality and problems of translated texts from the user's first language to a second language, and none has been conducted to examine translations produced by the updated system of GT (i.e., the Neural Machine Translation System). The purposes of this study are to examine the quality of abstracts translated from Thai, the user's first language, to English, the target language, using GT by evaluating their comprehensibility and usability levels and to examine frequent error types. Fifty-four abstracts were selected from academic journals in eight disciplines of Humanities and Social Sciences. They were rated by two experts using coding schemes. The results revealed overall comprehensibility and usability were both at a moderate level. That means the quality of the abstracts translated by GT may not meet the language requirements needed for academic writing. The most frequent errors produced by GT were those of capitalization, punctuation, and fragmentation.