The volume of published papers provided by the scientific community has increased over the last years in a drastic way. This fact has led to having a considerable growth of the topics covered by different publications. Despite topics under discussion on these publications were usually regarded as cutting edge subjects when released in conferences and journals, the restless evolution of science may have faded their relative importance away over the years. This issue undoubtedly poses big challenges to those researchers interested in gathering information to enrich their own background. Consequently, the development of a system able to automatically organize and provide relevance to scientific papers should play a crucial role to address the aforementioned problem. In this paper, the Webelance framework is presented. It makes use of a lexicon and Machine Learning techniques to accomplish these tasks. It has been built by using specific metrics for the scientific domain to measure the relative importance of papers. Several experiments using more than $50,000$ articles focused on the medicine domain have been addressed to illustrate the viability of the proposal. The obtained results both confirm the usability of the system and its good performance.

在过去的几年中，科学界提供的已发表论文的数量急剧增加。这一事实导致不同出版物所涵盖的主题有了相当大的增长。尽管有关这些出版物的讨论主题在会议和期刊上发布时通常被认为是最前沿的主题，但多年来科学的不停发展可能使它们的相对重要性逐渐消失。对于那些有兴趣收集信息以丰富自己背景的研究人员来说，这一问题无疑给他们带来了巨大的挑战。因此，能够自动组织并提供科学论文相关性的系统的开发应在解决上述问题方面发挥关键作用。在本文中，Webelance介绍了框架。它利用词典和机器学习技术来完成这些任务。它是通过使用针对科学领域的特定指标来衡量论文的相对重要性而构建的。多个实验使用了$50，000$已经讨论了有关医学领域的文章，以说明该提案的可行性。获得的结果都证实了系统的可用性及其良好的性能。