Lectura social y LIJ 2.0 en la universidad. Aplicaciones y redes sociales de lectura

Dentro del desarrollo de nuevos conceptos pedagógicos como las Tecnologías del Aprendizaje y el Conocimiento (TAC) y el Mobile Learning han aparecido algunos aspectos principales como la Lectura Social y la LIJ 2.0 fundamentales para la didáctica de la lengua y la literatura en entornos digitales. Entre las múltiples posibilidades que nos ofrecen los distintos servicios de redes sociales y microblogging, algunos objetivos para su uso didáctico pueden ser el fomento de la lectura y el desarrollo de la competencia lectoliteraria. Las redes sociales pueden ser un espacio de conversación y lectura donde, con dinámicas adecuadas, se trabaje la comprensión lectora desde muy distintas perspectivas. En el siguiente trabajo se hará una revisión de las distintas herramientas, desde los espacios más generalistas como Facebook o los clubes de lectura a través de servicios como Twitter, hasta llegar a aplicaciones y redes sociales específicas centradas en la literatura, como Aldiko, Wattpad, Ibooks, Anobii, Goodreads o Entrelectores, haciendo una descripción de las distintas posibilidades para la formación del alumnado y la promoción de la lectura.

A novel concept-level approach for ultra-concise opinion summarization

The Web 2.0 has resulted in a shift as to how users consume and interact with the information, and has introduced a wide range of new textual genres, such as reviews or microblogs, through which users communicate, exchange, and share opinions. The exploitation of all this user-generated content is of great value both for users and companies, in order to assist them in their decision-making processes. Given this context, the analysis and development of automatic methods that can help manage online information in a quicker manner are needed. Therefore, this article proposes and evaluates a novel concept-level approach for ultra-concise opinion abstractive summarization. Our approach is characterized by the integration of syntactic sentence simplification, sentence regeneration and internal concept representation into the summarization process, thus being able to generate abstractive summaries, which is one the most challenging issues for this task. In order to be able to analyze different settings for our approach, the use of the sentence regeneration module was made optional, leading to two different versions of the system (one with sentence regeneration and one without). For testing them, a corpus of 400 English texts, gathered from reviews and tweets belonging to two different domains, was used. Although both versions were shown to be reliable methods for generating this type of summaries, the results obtained indicate that the version without sentence regeneration yielded to better results, improving the results of a number of state-of-the-art systems by 9%, whereas the version with sentence regeneration proved to be more robust to noisy data.