Summary of the paper

Title Annotation and Analysis of Extractive Summaries for the Kyutech Corpus
Authors Takashi Yamamura and Kazutaka Shimada
Abstract Summarization of multi-party conversation is one of the important tasks in natural language processing. For conversation summarization tasks, corpora have an important role to analyze characteristics of conversations and to construct a method for summary generation. We are developing a freely available Japanese conversation corpus for a decision-making task. We call it the Kyutech corpus. The current version of the Kyutech corpus contains topic tags of each utterance and reference summaries of each conversation. In this paper, we explain an annotation task of extractive summaries. In the annotation task, we annotate an importance tag for each utterance and link utterances with sentences in reference summaries that already exist in the Kyutech corpus. By using the annotated extractive summaries, we can evaluate extractive summarization methods on the Kyutech corpus. In the experiment, we compare some methods based on machine learning techniques with some features.
Topics Discourse Annotation, Representation And Processing, Summarisation, Corpus (Creation, Annotation, Etc.)
Full paper Annotation and Analysis of Extractive Summaries for the Kyutech Corpus
Bibtex @InProceedings{YAMAMURA18.849,
  author = {Takashi Yamamura and Kazutaka Shimada},
  title = "{Annotation and Analysis of Extractive Summaries for the Kyutech Corpus}",
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year = {2018},
  month = {May 7-12, 2018},
  address = {Miyazaki, Japan},
  editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {979-10-95546-00-9},
  language = {english}
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