Question

A financial services company receives thousands of lengthy quarterly earnings PDF reports each week. Analysts need a low-code solution that first extracts the full text (including tables) from these PDFs, then produces a concise natural-language summary of each report's key financial highlights. The team has minimal ML expertise and wants to minimize custom code and infrastructure management. Which combination of Google Cloud services best meets these requirements?

Accepted Answer

Use the Document AI OCR/Document processor to extract text, then send the extracted text to a Gemini model from Model Garden (Vertex AI) to generate summaries.

Answer

Train a custom AutoML text classification model on the PDFs, then use the Cloud Natural Language API to generate summaries.

Answer

Import each PDF into a BigQuery ML remote model and run a SELECT query with ML.GENERATE_TEXT directly against the raw PDF bytes.

Answer

Use the Cloud Vision API to detect text in the PDFs, then build a custom TensorFlow seq2seq summarization model deployed on Vertex AI endpoints.