Group, Extract and Aggregate: Summarizing a Large Amount of Finance News for Forex Movement Prediction (D19-51)
Copied to clipboard
| Challenge: | Existing studies on forex prediction ignore related text completely and focus on forex trade data only, which loses important semantic information. |
| Approach: | They propose a BERT-based Hierarchical Aggregation Model to summarize forex news . they group news from different aspects and extract the most crucial news in each group . |
| Outcome: | The proposed model outperforms baseline methods and grouping methods and summarizes the influence patterns for forex trading. |
Similar Papers
End-to-End Segmentation-based News Summarization (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing summarization systems only provide one genetic summary of the whole article, making it difficult for users to navigate the reading. |
| Approach: | They propose a task of segmenting a news article into multiple sections and generating the corresponding summary to each section. |
| Outcome: | The proposed model outperforms state-of-the-art models on a 27k news article dataset . it can jointly segment a document and produce the summary for each section . |
Incorporating Fine-grained Events in Stock Movement Prediction (D19-51)
Copied to clipboard
| Challenge: | Existing studies mainly adopt coarse-grained events, which loses the specific semantic information of diverse event types. |
| Approach: | They propose to use a finance event dictionary to extract fine-grained events from finance news to train a neural model that uses the extracted events as the distant supervised label to train stock prediction. |
| Outcome: | The proposed method outperforms baselines and has good generalizability. |
Event-Driven Learning of Systematic Behaviours in Stock Markets (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Using financial news, we can predict stock market behaviours by extracting financial events from the news and ranking the importance of the events. |
| Approach: | They propose to combine open information extraction and neural co-reference resolution to extract financial events from news streams and extend hierarchical attention networks that include attentions on event, news and temporal levels. |
| Outcome: | The proposed method achieves significantly better accuracies and higher simulated annualized returns than state-of-the-art models when being applied to predicting Standard&Poor 500, Dow Jones, Nasdaq indices and 10 individual stocks. |
The Bull and the Bear: Summarizing Stock Market Discussions (2022.lrec-1)
Copied to clipboard
| Challenge: | a dataset of 7888 reddit posts and 400 posts is used to summarize stock market topics. |
| Approach: | They curate discussions on social media platforms and construct an abstractive summarization dataset. |
| Outcome: | The proposed dataset consists of 7888 Reddit posts and summaries for 400 posts . it is robustly evaluated and will be made publicly available . |
FAST: Financial News and Tweet Based Time Aware Network for Stock Trading (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing methods for stock movement prediction are limited and do not account for the fine-grain temporal irregularities in the release of large volumes of text. |
| Approach: | They propose a hierarchical, learning to rank approach that uses textual data to make time-aware predictions for ranking stocks based on expected profit. |
| Outcome: | The proposed method outperforms state-of-the-art methods by over 8% in terms of cumulative profit and risk-adjusted returns on two benchmarks: English tweets and Chinese financial news spanning two major stock indexes and four global markets. |
Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing models that use textual features and sentiments to make stock predictions are poor explainability and low signal-to-noise ratio. |
| Approach: | They propose a bi-level event detection model that detects corporate events from news articles and an elaborately-annotated dataset EDT for corporate event detection and news-based stock prediction benchmark. |
| Outcome: | The proposed strategy outperforms baselines in winning rate, excess returns over the market, and the average return on each transaction. |
Financial Event Extraction Using Wikipedia-Based Weak Supervision (D19-51)
Copied to clipboard
Liat Ein-Dor, Ariel Gera, Orith Toledo-Ronen, Alon Halfon, Benjamin Sznajder, Lena Dankin, Yonatan Bilu, Yoav Katz, Noam Slonim
| Challenge: | Existing methods for detecting financial and economic events from text have relied on a knowledge-base of financial events, or corresponding financial figures. |
| Approach: | They propose to use Wikipedia sections to extract weak labels for sentences describing economic events from text. |
| Outcome: | The proposed method can extract weak labels for sentences describing economic events from Wikipedia sentences. |
FinKario: Event-Enhanced Automated Construction of Financial Knowledge Graph (2026.acl-long)
Copied to clipboard
| Challenge: | Equity research reports are crucial resources for investors, but lack professional analysis and the rapid evolution of market events outpaces their update cycles. |
| Approach: | They propose an event-Enhanced automated construction of financial knowledge graph (FinKario) that automatically integrates real-time company fundamentals and market events through prompt-driven extraction guided by professional institutional templates. |
| Outcome: | The proposed model outperforms financial LLMs by 18.81% and institutional strategies by 17.85% on average in backtesting. |
HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing models that treat texts as linear sequences do not include hierarchical structure information. |
| Approach: | They propose to inject hierarchical structure information into an extractive summarization model by combining hierarchically structured text with a pre-trained Transformer language model. |
| Outcome: | The proposed model outperforms a baseline model on PubMed and arXiv datasets and the hierarchical structure information is not injected. |
Exploiting Discourse-Level Segmentation for Extractive Summarization (D19-54)
Copied to clipboard
| Challenge: | Existing approaches to extract summarize text are based on sentences as the elementary unit, but semantic segments containing supplementary information or descriptive details are often nonessential in the generated summaries. |
| Approach: | They propose to exploit discourse-level segmentation as a finer-grained means to more precisely pinpoint the core content in a document. |
| Outcome: | The proposed method improves extractive summarization performance on CNN/Daily Mail dataset. |