Relevant Content Selection through Positional Language ModelsAn Exploratory Analysis

  1. Marta Vicente
  2. Elena Lloret
Revista:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Ano de publicación: 2020

Número: 65

Páxinas: 75-82

Tipo: Artigo

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Outras publicacións en: Procesamiento del lenguaje natural

Resumo

Extractive Summarisation, like other areas in Natural Language Processing, has succumbed to the general trend marked by the success of neural approaches. However, the required resources-computational, temporal, data-are not always available. We present an experimental study of a method based on statistical techniques that, exploiting the semantic information from the source and its structure, provides competitive results against the state of the art. We propose a Discourse-Informed approach for Cost-effective Extractive Summarisation (DICES). DICES is an unsupervised, lightweight and adaptable framework that requires neither training data nor high-performance computing resources to achieve promising results.

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Información de financiamento

This research results from work partially funded by Generalitat Valenciana (SIIA PROMETEU/2018/089) and the Spanish Government—ModeLang (RTI2018-094653-B-C22) and INTEGER (RTI2018-094649-BI00). It is also based upon work from COST Action Multi3Generation (CA18231).

Financiadores

Referencias bibliográficas

  • Boudin, F., J. Y. Nie, and M. Dawes. 2010. Positional language models for clinical information retrieval. In Proc. of EMNLP, pages 108–115.
  • Chen, Y.-C. and M. Bansal. 2018. Fast abstractive summarization with reinforceselected sentence rewriting. In Proc. of the ACL, Vol. 1, pages 675–686.
  • Cheng, J. and M. Lapata. 2016. Neural summarization by extracting sentences and words. In Proc. of ACL, pages 484–494.
  • Cho, S., L. Lebanoff, H. Foroosh, and F. Liu. 2019. Improving the similarity measure of determinantal point processes for extractive multi-document summarization. In Proc. of the ACL, Vol. 1, pages 1027–1038.
  • Erkan, G. and D. R. Radev. 2004. LexRank: Graph-based lexical centrality as salience in text summarization. Journal of Artificial Intelligence Research, 22:457–479.
  • Gehrmann, S., Y. Deng, and A. Rush. 2018. Bottom-up abstractive summarization. In Proc. of EMNLP, pages 4098–4109.
  • Hermann, K. M., T. Kocisky, E. Grefenstette, L. Espeholt, W. Kay, M. Suleyman, and P. Blunsom. 2015. Teaching machines to read and comprehend. In Advances in neural information processing systems, pages 1693–1701.
  • Hirao, T., Y. Yoshida, M. Nishino, N. Yasuda, and M. Nagata. 2013. Singledocument summarization as a tree knapsack problem. In Proc. of EMNLP, pages 1515–1520. Kilgarriff, A. and C. Fellbaum. 2000. WordNet: An Electronic Lexical Database. Language, 76(3):706.
  • Lin, C.-Y. 2004. ROUGE: A package for automatic evaluation of summaries. In Text Summarization Branches Out, pages 74–81.
  • Liu, Y. and M. Lapata. 2019. Text summarization with pretrained encoders. In Proc. of EMNLP-IJCNLP, pages 3721–3731.
  • Liu, Y., I. Titov, and M. Lapata. 2019. Single document summarization as tree induction. In Proc. of the NAACL, Vol. 1, pages 1745–1755.
  • Liu, Z. and N. Chen. 2019. Exploiting Discourse-Level Segmentation for Extractive Summarization. In Proc. of the 2nd Workshop on New Frontiers in Summarization, pages 116–121.
  • Mann, W. C. and S. A. Thompson. 1987. Rhetorical Structure Theory: Description and Construction of Text Structures. In Natural Language Generation. Springer Netherlands, pages 85–95.
  • Nallapati, R., B. Zhou, C. dos Santos, C¸ . Gul¸cehre, and B. Xiang. 2016. Abstractive text summarization using sequence-to-sequence RNNs and beyond. In Proc. of SIGNLL, pages 280–290.
  • Nenkova, A. and K. McKeown. 2011. Automatic Summarization. Foundations and Trends R in Information Retrieval, 5(2):103–233.
  • Padró, L. and E. Stanilovsky. 2012. FreeLing 3.0: Towards Wider Multilinguality FreeLing project developer. Proc. of LREC.
  • See, A., P. J. Liu, and C. D. Manning. 2017. Get to the point: Summarization with pointer-generator networks. Proc. of ACL, 1:1073–1083.
  • Strubell, E., A. Ganesh, and A. McCallum. 2019. Energy and policy considerations for deep learning in NLP. In Proc. of the ACL, pages 3645–3650.
  • Vicente, M., C. Barros, and E. Lloret. 2018. Statistical language modelling for automatic story generation. Journal of Intelligent & Fuzzy Systems, 34(5):3069–3079.
  • Wu, Y. and B. Hu. 2018. Learning to extract coherent summary via deep reinforcement learning. In AAAI Conf. on Artificial Intelligence, pages 5602–5609.
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