Financial Text Summarization & Insight Generation
Map-reduce summarization for length, RAG for relevance, ROUGE for honesty.
The problem
Analysts read hundreds of pages of annual reports and credit reviews. A full report exceeds any transformer's context window, and summarizing blindly buries the risk disclosures the reader actually opened it for.
The build
Sentence-aware chunking with map-reduce summarization to handle length, and a retrieval-augmented pipeline to answer targeted questions with grounded evidence. Evaluated with ROUGE against reference summaries.
Architecture
— one teal highlight: the retrieval pathWhat I found
Length and relevance are separate problems and need separate mechanisms. Chunking solves the context window; only retrieval solves 'what are the key risks in this filing'.
The reps
Keeping summaries faithful under map-reduce — where each chunk summary is itself an input — is where the errors compound.
Results
- ROUGEquantitative summary evaluation
- 2paths: summarize + RAG Q&A
- 50pp → 1filing to paragraph digest
What's verified
Every case study carries this blockWhat was measured, and what was not. Nothing in the second column is estimated into the first.
The notebook is committed already-executed, with ROUGE scores and charts rendering on GitHub.
Evaluated on a sample corpus, not a full filings archive.