Textual Information Aggregation from Miscellaneous Sources
Event details
| Date | 29.05.2018 |
| Hour | 14:00 › 16:00 |
| Speaker | Diego Antognini |
| Location | |
| Category | Conferences - Seminars |
EDIC candidacy exam
Exam president: Prof. Martin Jaggi
Thesis advisor: Prof. Boi Faltings
Co-examiner: Prof. Robert West
Abstract
Nowadays, information from various domains is overwhelming us from all sides, whether it comes from medias, social networks or simply from the web. Moreover, two contents might share the same semantics but can be expressed in a lot of different ways, making the process of aggregation even harder. Furthermore, the elicited information can be easily altered and propagated. Therefore, there is a need to develop methods for understanding contents or opinions from different documents and determining if they mean the same, are different or even contradictory. The applications of such methods could be information tracing, multi-document summarization and many more.
Background papers
Towards Coherent Multi-Document Summarization, by Christensen C., et al.
Graph Attention Networks, by Velickovic P., et al.
Epidemiological Modeling of News and Rumors on Twitter , by Jin F., et al
Exam president: Prof. Martin Jaggi
Thesis advisor: Prof. Boi Faltings
Co-examiner: Prof. Robert West
Abstract
Nowadays, information from various domains is overwhelming us from all sides, whether it comes from medias, social networks or simply from the web. Moreover, two contents might share the same semantics but can be expressed in a lot of different ways, making the process of aggregation even harder. Furthermore, the elicited information can be easily altered and propagated. Therefore, there is a need to develop methods for understanding contents or opinions from different documents and determining if they mean the same, are different or even contradictory. The applications of such methods could be information tracing, multi-document summarization and many more.
Background papers
Towards Coherent Multi-Document Summarization, by Christensen C., et al.
Graph Attention Networks, by Velickovic P., et al.
Epidemiological Modeling of News and Rumors on Twitter , by Jin F., et al
Practical information
- General public
- Free