A skills detector system is provided with an on-line social network system. The skills detector system is configured to determine which skills are referenced in an electronic presentation and generate respective importance scores of the determined skills as related to the presentation. Respective importance scores of the determined skills as related to the presentation can be used beneficially to select one or more electronic courses that are relevant in teaching skills discussed or mentioned in the presentation and recommend those courses to viewers of the presentation.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method comprising: maintaining member profiles representing members in an on-line social network system, a profile from the member profiles comprising a skills section populated with one or more values corresponding to respective entries from a skills database; accessing an electronic presentation, the electronic presentation comprising one or more sections; parsing a target section from the one or more sections to identify in the target section one or more phrases representing respective one or more skills that correspond to respective entries in the skills database; using at least one processor, generating respective importance scores for each of the one or more skills; and storing the identified one or more skills and their respective importance scores as associated with the target section. 2. The method of claim 1, comprising: detecting that a member of the on-line social network system is viewing the target section of the electronic presentation; and causing presentation of references to the one or more skills and their respective importance values. 3. The method of claim 1, comprising generating a representation of the target section as a skills graph, nodes in the skills graph representing the one or more skills, wherein the generating of an importance score for a skill represented by a node in the skills graph comprises using a centrality score, the centrality score generated by applying to the skills graph a graph analysis algorithm. 4. The method of claim 3, wherein the generating of the importance score for the skill represented by the node in the skills graph comprises utilizing a document structure score calculated based on a position of a phrase representing the skill in a structure of the electronic presentation. 5. The method of claim 4, wherein a first document structure score calculated for a first skill represented by a phrase that appears in the title of the electronic presentation is greater than a second document structure score calculated for a second skill represented by a phrase that appears in the body of the electronic presentation and is absent from the title. 6. The method of claim 1, comprising generating a representation of the target section as a feature vector comprising dimensions representing respective skills and their characteristics in relationship to the electronic presentation, wherein the generating of the respective importance scores comprises learning a model that takes the feature vector as input. 7. The method of claim 6, wherein a dimension in the feature vector is an indication of absence or presence of a skill from the respective skills in the title of the electronic presentation. 8. The method of claim 6, wherein a dimension in the feature vector is an indication of a visual emphasis associated with a skill in the electronic presentation 9. The method of claim 1, comprising: detecting an event indicating rendering of the electronic presentation on a display device; and using the respective importance scores to identify one or more electronic courses as relevant to the electronic presentation. 10. The method of claim 1, wherein the electronic presentation is an electronic slideshow presentation stored by the on-line social network system. 11. A computer-implemented system comprising: an access module, implemented using at least one processor, to access an electronic presentation comprising one or more sections; a parser, implemented using at least one processor, go parse a target section from the one or more sections to identify in the target section one or more phrases representing respective one or more skills that correspond to respective entries in a skills database, the skills database maintained by a an on-line social network system, the on-line social network system maintaining member profiles representing members in the on-line social network system, a profile from the member profiles comprising a skills section populated with one or more values corresponding to respective entries from the skills database; an importance scores generator, implemented using at least one processor, to generate respective importance scores for each of the one or more skills; and a storing module, implemented using at least one processor, to store the identified one or more skills and their respective importance scores as associated with the target section. 12. The system of claim 11, comprising: a viewer detector, implemented using at least one processor, to detect that a member of the on-line social network system is viewing the target section of the electronic presentation, and a presentation module, implemented using at least one processor, to cause presentation of references to the one or more skills and their respective importance values. 13. The system of claim 11, wherein the importance scores generator is to generate a representation of the target section as a skills graph, nodes in the skills graph representing the one or more skills, wherein the generating of an importance score for a skill represented by a node in the skills graph comprises using a centrality score, the centrality score generated by applying to the skills graph a graph analysis algorithm. 14. The system of claim 13, wherein the importance scores generator is to generate the importance score for the skill represented by the node in the skills graph comprises utilizing a document structure score calculated based on a position of a phrase representing the skill in a structure of the electronic presentation. 15. The system of claim 14, wherein a first document structure score calculated for a first skill represented by a phrase that appears in the title of the electronic presentation is greater than a second document structure score calculated for a second skill represented by a phrase that appears in the body of the electronic presentation and is absent from the title. 16. The system of claim 11, wherein the importance scores generator is to generate a representation of the target section as a feature vector comprising dimensions representing respective skills and their characteristics in relationship to the electronic presentation, wherein the generating of the respective importance scores comprises learning a model that takes the feature vector as input. 17. The system of claim 16, wherein a dimension in the feature vector is an indication of absence or presence of a skill from the respective skills in the title of the electronic presentation. 18. The system of claim 16, wherein a dimension in the feature vector is an indication of a visual emphasis associated with a skill in the electronic presentation. 19. The system of claim 11, comprising: a viewer detector, implemented using at least one processor, to detect an event indicating rendering of the electronic presentation on a display device; and a course detector, implemented using at least one processor, to identify one or more electronic courses as relevant to the electronic presentation using the respective importance scores. 20. A machine-readable non-transitory storage medium having instruction data executable by a machine to cause the machine to perform operations comprising: maintaining member profiles representing members in an on-line social network system, a profile from the member profiles comprising a skills section populated with one or more values corresponding to respective entries from a skills database; accessing an electronic presentation, the electronic presentation comprising one or more sections; parsing a target section from the one or more sections to identify in the target section one or more phrases representing respective one or more skills that correspond to respective entries in the skills database; generating respective importance scores for each of the one or more skills; and storing the identified one or more skills and their respective importance scores as associated with the target section.