Update README.md

README.md

@@ -11,9 +11,13 @@ dataset_format: csv
 
 # EDGAR-CORPUS : 10K Financial Report Summarization 
 
-The EDGAR-CORPUS Financial Summarization Dataset is a collection of financial statements extracted from SEC EDGAR filings 
+Extracted from SEC EDGAR filings (1993-2020). This dataset enhances financial report summarization by leveraging a hybrid AI model strategy.
 
-**Source:** SEC EDGAR Filings (1993-2020)  **eloukas/edgar-corpus**
+Using:
+
+ChatGPT-3.5 Turbo(~70%), 
+
+Claude 3.5 (~30% to generate structured, accurate, and concise summaries)
 
 ### **Dataset Composition**  
 Summaries in this dataset are generated using a **hybrid AI model strategy**, balancing quality and efficiency:  
@@ -29,9 +33,6 @@ The dataset incorporates **spaCy** for **NLP-based word tracking**, applied only
   - **Adaptive model selection** (Sonnet for long texts, Haiku for short ones)  
   - **Keyword-driven extraction** of **essential financial metrics** (e.g., EBITDA, market cap, stock price)  
 
-This integration ensures **concise yet accurate** summaries while **avoiding AI repetition and redundancy** in dataset creation.  
----
-
 ### **Example Row from the Dataset**
 Below is a real example from the dataset, showing a financial statement **input**, the **AI-generated summary**, and the **model used**:
 
@@ -55,4 +56,16 @@ from datasets import load_dataset
 dataset = load_dataset("kritsadaK/EDGAR-CORPUS-Financial-Summarization")
 ```
 
+---
+
+## Limitations: Summary Accuracy & AI Hallucinations
+AI-generated financial summaries are not 100% accurate without safeguards. This dataset incorporates:
+
+- Entity Matching & Verification: spaCy NER cross-checks extracted financial terms.
+- Consistency Checks: Rule-based validation prevents misinterpretation (e.g., net loss ≠ profit trend).
+- Standardized Format: Aligns Claude and ChatGPT outputs for consistency.
+- Bias & Overfitting Prevention: Balances structured data extraction with AI-generated summaries.
+## Despite these safeguards, users should verify summaries before relying on them for financial decisions.
+---
+
 The **Financial Statements Summary 10K Dataset** was developed as part of the **CSX4210: Natural Language Processing** project at **Assumption University**.