Abstract
In our research work, we represent the content of the sentence in graphical form after extracting triples from the sentences. In this paper, we will discuss novel methods to generate an extractive summary by scoring the triples. Our work has also touched upon sequence-to-sequence encoding of the content of the sentence, to classify it as a summary or a non-summary sentence. Our findings help to decide the nature of the sentences forming the summary and the length of the system generated summary as compared to the length of the reference summary.- Anthology ID:
- 2020.fnp-1.22
- Volume:
- Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Dr Mahmoud El-Haj, Dr Vasiliki Athanasakou, Dr Sira Ferradans, Dr Catherine Salzedo, Dr Ans Elhag, Dr Houda Bouamor, Dr Marina Litvak, Dr Paul Rayson, Dr George Giannakopoulos, Nikiforos Pittaras
- Venue:
- FNP
- SIG:
- Publisher:
- COLING
- Note:
- Pages:
- 130–136
- Language:
- URL:
- https://aclanthology.org/2020.fnp-1.22
- DOI:
- Cite (ACL):
- Amit Vhatkar, Pushpak Bhattacharyya, and Kavi Arya. 2020. Knowledge Graph and Deep Neural Network for Extractive Text Summarization by Utilizing Triples. In Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, pages 130–136, Barcelona, Spain (Online). COLING.
- Cite (Informal):
- Knowledge Graph and Deep Neural Network for Extractive Text Summarization by Utilizing Triples (Vhatkar et al., FNP 2020)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2020.fnp-1.22.pdf