@inproceedings{el-ebshihy-etal-2020-artu,
    title = "{ARTU} / {TU} {W}ien and Artificial Researcher@ {L}ong{S}umm 20",
    author = "El-Ebshihy, Alaa  and
      Ningtyas, Annisa Maulida  and
      Andersson, Linda  and
      Piroi, Florina  and
      Rauber, Andreas",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.sdp-1.36",
    doi = "10.18653/v1/2020.sdp-1.36",
    pages = "310--317",
    abstract = "In this paper, we present our approach to solve the LongSumm 2020 Shared Task, at the 1st Workshop on Scholarly Document Processing. The objective of the long summaries task is to generate long summaries that cover salient information in scientific articles. The task is to generate abstractive and extractive summaries of a given scientific article. In the proposed approach, we are inspired by the concept of Argumentative Zoning (AZ) that de- fines the main rhetorical structure in scientific articles. We define two aspects that should be covered in scientific paper summary, namely Claim/Method and Conclusion/Result aspects. We use Solr index to expand the sentences of the paper abstract. We formulate each abstract sentence in a given publication as query to retrieve similar sentences from the text body of the document itself. We utilize a sentence selection algorithm described in previous literature to select sentences for the final summary that covers the two aforementioned aspects.",
}