A systematic review of question answering systems for non-factoid questions

Question Answering (QA) is a field of study addressed to develop automatic methods for answering questions expressed in natural language. Recently, the emergence of the new generation of intelligent assistants, such as Siri, Alexa, and Google Assistant, has intensified the importance of an effective and efficient QA system able to handle questions with different complexities. Regarding the type of question to be answered, QA systems have been divided into two sub-areas: (i) factoid questions that require a single fact – e.g., a name of a person or a date, and (ii) non-factoid questions that need a more complex answer – e.g., descriptions, opinions, or explanations. While factoid QA systems have overcome human performance on some benchmarks, automatic systems for answering non-factoid questions remain a challenge and an open research problem. This work provides an overview of recent research addressing non-factoid questions. It focuses on which methods have been applied in each task, the data sets available, challenges and limitations, and possible research directions. From a total of 455 recent studies, we selected 75 papers based on our quality control system and exclusion criteria for an in-depth analysis. This systematic review helped to answer what are the tasks and methods involved in non-factoid, what are the data sets available, what the limitations are, and what is the recommendations for future research.