Joint abstractive and extractive method for long financial documentsummarization

Zmandar, Nadhem and Singh, Abhishek and El-Haj, Mahmoud and Rayson, Paul (2021) Joint abstractive and extractive method for long financial documentsummarization. In: 3rd Financial Narrative Processing Workshop (FNP 2021), 2021-09-152021-09-16, Virtual.

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In this paper we show the results of our participation in the FNS 2021 shared task. In our work we propose an end to end financial narrative summarization system that first selects salient sentences from the document and then paraphrases extracted sentences. This method generates an overall concise summary that maximises the ROUGE metric with the gold standard summary. The end to end system is developed using a hybrid extractive and abstractive architecture followed by joint training using policy-based reinforcement learning to bridge together the two networks. Empirically, we achieve better scores than the proposed baselines and toplines of FNS 2021 (LexRank, TextRank, MUSE topline and POLY baseline) and we were ranked 2nd in the shared task competition. Keywords: Summarization, Neural networks, Reinforcement learning, sequence to sequence learning; actor-critic methods; policy gradients.

Item Type:
Contribution to Conference (Paper)
Journal or Publication Title:
3rd Financial Narrative Processing Workshop (FNP 2021)
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04 Nov 2021 17:25
Last Modified:
12 Sep 2023 05:48