Name
Title | ||
|---|---|---|
A Methodology For Evaluating Timeline Generation Algorithms Based On Deep Semantic Units |
Abstract | ||
|---|---|---|
Timeline generation is a summarisation task which transforms a narrative, roughly chronological input text into a set of timestamped summary sentences, each expressing an atomic historical event. We present a methodology for evaluating systems which create such timelines, based on a novel corpus consisting of 36 human-created timelines. Our evaluation relies on deep semantic units which we call historical content units. An advantage of our approach is that it does not require human annotation of new system summaries. |
Year | Venue | Field |
|---|---|---|
2015 | PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL) AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (IJCNLP), VOL 2 | Annotation,Computer science,Narrative,Timeline,Artificial intelligence,Natural language processing |
DocType | Volume | Citations |
Conference | P15-2 | 1 |
PageRank | References | Authors |
0.36 | 11 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sandro Bauer | 1 | 47 | 3.75 |
| Simone Teufel | 2 | 1066 | 82.38 |