# A Comprehensive Review on Summarizing Financial News Using Deep Learning

arXiv: https://arxiv.org/abs/2109.10118 · HTML: https://mlsanity.com/paper/2109.10118/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 101 references that are in the map.
- First posted on arXiv 21 Sep 2021.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- ["What is Relevant in a Text Document?": An Interpretable Machine Learning Approach](https://mlsanity.com/paper/1612.07843/) (2016)
- [Fine-grained Sentiment Classification using BERT](https://mlsanity.com/paper/1910.03474/) (2019)
- [Sentiment Analysis of Twitter Data for Predicting Stock Market Movements](https://mlsanity.com/paper/1610.09225/) (2016)
- [Adversarial Training for Aspect-Based Sentiment Analysis with BERT](https://mlsanity.com/paper/2001.11316/) (2020)
- [Recurrent Neural Networks for Time Series Forecasting](https://mlsanity.com/paper/1901.00069/) (2019)
- [A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News](https://mlsanity.com/paper/2007.12620/) (2020)
- [Overcoming the vanishing gradient problem in plain recurrent networks](https://mlsanity.com/paper/1801.06105/) (2018)
- [Stock trend prediction using news sentiment analysis](https://mlsanity.com/paper/1607.01958/) (2016)
- [Leveraging Financial News for Stock Trend Prediction with Attention-Based Recurrent Neural Network](https://mlsanity.com/paper/1811.06173/) (2018)
- [ESG2Risk: A Deep Learning Framework from ESG News to Stock Volatility Prediction](https://mlsanity.com/paper/2005.02527/) (2020)
- [Global Sentiment Analysis Of COVID-19 Tweets Over Time](https://mlsanity.com/paper/2010.14234/) (2020)
- [Learning to Determine the Quality of News Headlines](https://mlsanity.com/paper/1911.11139/) (2019)
- [Word frequency and sentiment analysis of twitter messages during Coronavirus pandemic](https://mlsanity.com/paper/2004.03925/) (2020)
- [Sentiment Analysis on Customer Responses](https://mlsanity.com/paper/2007.02237/) (2020)

## Abstract (arXiv)

Investors make investment decisions depending on several factors such as fundamental analysis, technical analysis, and quantitative analysis. Another factor on which investors can make investment decisions is through sentiment analysis of news headlines, the sole purpose of this study. Natural Language Processing techniques are typically used to deal with such a large amount of data and get valuable information out of it. NLP algorithms convert raw text into numerical representations that machines can easily understand and interpret. This conversion can be done using various embedding techniques. In this research, embedding techniques used are BoW, TF-IDF, Word2Vec, BERT, GloVe, and FastText, and then fed to deep learning models such as RNN and LSTM. This work aims to evaluate these model's performance to choose the robust model in identifying the significant factors influencing the prediction. During this research, it was expected that Deep Leaming would be applied to get the desired results or achieve better accuracy than the state-of-the-art. The models are compared to check their outputs to know which one has performed better.