Loading...
Please wait, while we are loading the content...
Similar Documents
Unsupervised learning strategies for automatic generation of personalized summaries
| Content Provider | Semantic Scholar |
|---|---|
| Author | Woloszyn, Vinicius |
| Copyright Year | 2019 |
| Abstract | It is relatively hard for readers to deal objectively with large documents in order to absorb the key idea about a particular subject. In this sense, automatic text summarization plays an important role by systematically digest a large number of documents to produce indepth abstracts. Despite fifty years of studies in automatic summarization of texts, one of the still persistent shortcomings is that the individual interests of the readers are still not considered. Regarding the automatic techniques for generation of summaries, it mostly relies on supervised Machine Learning algorithms such as classification and regression, however, the quality of results is dependent on the existence of a large, domain-dependent training data set. On the other hand, unsupervised learning strategies are an attractive alternative to avoid the labor-intense and error-prone task of manual annotation of training data sets. To accomplish such objective, this work puts forward a novel unsupervised and semi-supervised algorithms to automatically generate tailored summaries. Our experiments showed that we can effectively identify a significant number of interesting passages for the readers with less data for the training step. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://www.lume.ufrgs.br/bitstream/handle/10183/200036/001102767.pdf?isAllowed=y&sequence=1 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
