Query-Based Summarization and Sentiment Analysis for Indian Financial Text by leveraging Dense Passage Retriever, RoBERTa, and FinBERT

Numair Shaikh, Jayesh Patil, Sheetal Sonawane

Abstract
With the ever-expanding pool of information accessible on the Internet, it has become increasingly challenging for readers to sift through voluminous data and derive meaningful insights. This is particularly noteworthy and critical in the context of documents such as financial reports and large-scale media reports. In the realm of finance, documents are typically lengthy and comprise numerical values. This research delves into the extraction of insights through text summaries from financial data, based on the user’s interests, and the identification of clues from these insights. This research presents a straightforward, allencompassing framework for conducting querybased summarization of financial documents, as well as analyzing the sentiment of the summary. The system’s performance is evaluated using benchmarked metrics, and it is compared to State-of-The-Art (SoTA) algorithms. Extensive experimentation indicates that the proposed system surpasses existing pre-trained language models.
Anthology ID:
2023.icon-1.32
Volume:
Proceedings of the 20th International Conference on Natural Language Processing (ICON)
Month:
December
Year:
2023
Address:
Goa University, Goa, India
Editors:
Jyoti D. Pawar, Sobha Lalitha Devi
Venue:
ICON
SIG:
SIGLEX
Publisher:
NLP Association of India (NLPAI)
Note:
Pages:
398–407
Language:
URL:
https://aclanthology.org/2023.icon-1.32
DOI:
Bibkey:
Cite (ACL):
Numair Shaikh, Jayesh Patil, and Sheetal Sonawane. 2023. Query-Based Summarization and Sentiment Analysis for Indian Financial Text by leveraging Dense Passage Retriever, RoBERTa, and FinBERT. In Proceedings of the 20th International Conference on Natural Language Processing (ICON), pages 398–407, Goa University, Goa, India. NLP Association of India (NLPAI).
Cite (Informal):
Query-Based Summarization and Sentiment Analysis for Indian Financial Text by leveraging Dense Passage Retriever, RoBERTa, and FinBERT (Shaikh et al., ICON 2023)
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