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Autor*innen: Tzouridis, Emmanouil; Brefeld, Ulf
Titel: Learning shortest paths for word graphs
Aus: Atzmueller, Martin ; Scholz, Christoph (Hrsg.): The Fourth International Workshop on Mining Ubiquitous and Social Environments: MUSE' 13. September 23, 2013 (ECML/PKDD 2013), Prag: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013 , S. 45-57
URL: http://www.kde.cs.uni-kassel.de/ws/muse2013/proceedings.pdf
Dokumenttyp: 4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Sprache: Englisch
Schlagwörter: Algorithmus; Grafische Darstellung; Informatik; Lernen; Satz; Struktur; Wort
Abstract: The vast amount of information on the Web drives the need
for aggregation and summarisation techniques. We study event extrac- tion as a text summarisation
task using redundant sentences which is also known as sentence compression. Given a set of
sentences describing the same event, we aim at generating a summarisation that is (i) a single sentence, (ii) simply structured and easily understandable, and (iii) minimal in terms of the number
of words/tokens. Existing approaches for sentence compression are often based on finding the
shortest path in word graphs that is spanned by related input sentences. These approaches, however,
deploy manually crafted heuristics for edge weights and lack theoretical justification. In this
paper, we cast sentence compression as a structured prediction problem. Edges of the compression
graph are represented by features drawn from adjacent nodes so that corresponding weights are
learned by a generalised linear model. Decoding is performed in polynomial time by a generalised
shortest path algorithm using loss augmented inference. We report on preliminary results on
artificial and real world data.
DIPF-Abteilung: Informationszentrum Bildung