The automation of text summarisation of biomedical publications is a pressing need due to the plethora of information available on-line. This paper explores the impact of several supervised machine learning approaches for extracting multi-document summaries for given queries. In particular, we compare classification and regression approaches for query-based extractive summarisation using data provided by the BioASQ Challenge. We tackled the problem of annotating sentences for training classification systems and show that a simple annotation approach outperforms regression-based summarisation.
In contrast to other domains, research on automatic text processing in the medical domain is still very much in its infancy. In the recent past, there has been steady ongoing research in biomedical text processing @cite_9 . Factors such as the requirement of large volume of data, highly complex domain-specific terminologies and domain-specific format, and typology of questions @cite_16 makes it complex to process biomedical text. Most of the researchers working on summarisation for the medical domain apply the same kinds of techniques developed in other domains. Three main supervised machine learning approaches have been used for text summarisation: classification, regression, and learning to rank. The concept of summarising text by using supervised classification approaches was pioneered by . They categorised each sentence as worthy of extraction or not by a classification function, using a Na "ive Bayes classifier. In this classification approach the sentences are treated individually. At first, most machine learning systems assumed feature independence and relied on Na "ive Bayes methods @cite_4 . However, later models shifted the focus towards breaking the assumption that features are independent of each other @cite_19 .
@cite_19 @cite_9 @cite_16 @cite_4
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In this paper we compare performance of several heuristics in generating informative generic query-oriented extracts for newspaper articles in order to learn how topic prominence affects the performance of each heuristic. We study how different query types can affect the performance of each heuristic and discuss the possibility of using machine learning algorithms to automatically learn good combination functions to combine several heuristics. We also briefly describe the design, implementation, and performance of a multilingual text summarization system SUMMARIST. It is now almost 15 years since the publication of the first paper on text mining in the genomics domain, and decades since the first paper on text mining in the medical domain. Enormous progress has been made in the areas of information retrieval, evaluation methodologies and resource construction. Some problems, such as abbreviation-handling, can essentially be considered solved problems, and others, such as identification of gene mentions in text, seem likely to be solved soon. However, a number of problems at the frontiers of biomedical text mining continue to present interesting challenges and opportunities for great improvements and interesting research. In this article we review the current state of the art in biomedical text mining or ‘BioNLP’ in general, focusing primarily on papers published within the past year. Objectives: In this survey, we reviewed the current state of the art in biomedical QA (Question Answering), within a broader framework of semantic knowledge-based QA approaches, and projected directions for the future research development in this critical area of intersection between Artificial Intelligence, Information Retrieval, and Biomedical Informatics. Materials and methods: We devised a conceptual framework within which to categorize current QA approaches. In particular, we used ''semantic knowledge-based QA'' as a category under which to subsume QA techniques and approaches, both corpus-based and knowledge base (KB)-based, that utilize semantic knowledge-informed techniques in the QA process, and we further classified those approaches into three subcategories: (1) semantics-based, (2) inference-based, and (3) logic-based. Based on the framework, we first conducted a survey of open-domain or non-biomedical-domain QA approaches that belong to each of the three subcategories. We then conducted an in-depth review of biomedical QA, by first noting the characteristics of, and resources available for, biomedical QA and then reviewing medical QA approaches and biological QA approaches, in turn. The research articles reviewed in this paper were found and selected through online searches. Results: Our review suggested the following tasks ahead for the future research development in this area: (1) Construction of domain-specific typology and taxonomy of questions (biological QA), (2) Development of more sophisticated techniques for natural language (NL) question analysis and classification, (3) Development of effective methods for answer generation from potentially conflicting evidences, (4) More extensive and integrated utilization of semantic knowledge throughout the QA process, and (5) Incorporation of logic and reasoning mechanisms for answer inference. Conclusion: Corresponding to the growth of biomedical information, there is a growing need for QA systems that can help users better utilize the ever-accumulating information. Continued research toward development of more sophisticated techniques for processing NL text, for utilizing semantic knowledge, and for incorporating logic and reasoning mechanisms, will lead to more useful QA systems. The increasing availability of online information has necessitated intensive research in the area of automatic text summarization within the Natural Language Processing (NLP) community. Over the past half a century, the problem has been addressed from many dierent perspectives, in varying domains and using various paradigms. This survey intends to investigate some of the most relevant approaches both in the areas of single-document and multipledocument summarization, giving special emphasis to empirical methods and extractive techniques. Some promising approaches that concentrate on specic details of the summarization problem are also discussed. Special attention is devoted to automatic evaluation of summarization systems, as future research on summarization is strongly dependent on progress in this area.