Multi-document extractive summarization using semantic graph

La generación automática de resúmenes consiste en sintetizar en un texto corto la información más relevante contenida en documentos, y permite reducir los problemas generados por la sobrecarga de información. En este trabajo se presenta un método no supervisado de generación de resúmenes extractivos a partir de múltiples documentos. En esta propuesta, la conceptualización y estructura semántica subyacente del contenido textual se representa en un grafo semántico usando WordNet y se aplica un algoritmo de agrupamiento de conceptos para identificar los tópicos tratados en los documentos, con los cuales se evalúa la relevancia de las oraciones para construir el resumen. El método fue evaluado con corpus de textos de MultiLing 2015, y se usaron métricas de ROUGE para medir la calidad de los resúmenes generados. Los resultados obtenidos se compararon con los de otros sistemas participantes en MultiLing 2015, evidenciándose mejoras en la mayoría de los casos.The automatic texts summarization consists in synthesizing in a short text the most relevant information contained in text documents, and allows to reduce the generated problems by the information overload. In this paper, an unsupervised method for extractive multi-document summarization is presented. In this proposal, the conceptualization and underlying semantics structure of the textual content is represented in a semantic graph using WordNet, and a concept clustering algorithm is applied to identifying the topics of the documents set, with which the relevance of the sentences is evaluated to build the summary. The method was evaluated with texts corpus from MultiLing 2015, and ROUGE metrics were used to measure the quality of the generated summaries. The obtained results were compared with those other participant systems in MultiLing 2015, evidencing improves in most of the cases.Este trabajo ha sido parcialmente soportado por el Fondo Europeo de Desarrollo Regional (FEDER) y el Ministerio Español de Economía y Competitividad, bajo la subvención del proyecto METODOS RIGUROSOS PARA EL INTERNET DEL FUTURO (MERINET) Ref. TIN2016-76843-C4-2-R (AEI/FEDER, UE)

Tacit knowledge for business intelligence framework using cognitive-based approach

Tacit knowledge becoming a key issue in business intelligence approach to knowledge systems. Capturing the tacit knowledge is not a straightforward task, since it consists of unstructured data and related to a variety of information that is not always accessible through traditional means. This work presents a systematic approach for capturing tacit knowledge to be used in a business intelligence framework. The approach is based on the theory of systematic functional linguistics, developed into interview protocols to be asked to tacit knowledge owners. The data transformed into cognitive maps to supply the data warehouse. The framework was tested on 23 librarians from several university libraries in West Java and Yogyakarta, Indonesia. The algorithm starts with a content targeted interview to identify the list of problems faced by librarians. The problems were then converted into a questionnaire to identify qualities of the problems such as frequency, urgency, severity, and importance. From the questionnaire results, the best tacit knowledge performers were identified. They are respondents who can solve the problems, while the majority of the respondents are unable to solve them. The best performers were then subjected to grammar targeted interview to collect the solutions they made to the problems. The transcription of the interview results is then converted into cognitive maps that visualize the solutions. These cognitive maps are then stored in a data warehouse and ready to collect anytime for analytics purposes. The framework is validated through Power BI and reviewed by seven experts. Its applicability to other domains is justified as long as the domain, e.g., manufacturing, have experienced problems related to technical, managerial, and empirical problems faced by employees at work. This research contributes to the methods of capturing tacit knowledge using a cognitive-based approach, which important to ensure the continuity of business in various domains

Geographic information extraction from texts

A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction