Ertas for Text Summarization

The Challenge

Every organization drowns in text — meeting transcripts, research papers, legal briefs, customer feedback, news articles, and internal reports. Summarization tools promise to solve this overload, but generic models produce summaries that miss domain-critical details. A generic summary of a clinical trial report might capture the headline result but omit the adverse event data that a safety reviewer needs. A summary of a legal motion might paraphrase the argument but lose the specific statutory citations that a litigator relies on.

The fundamental problem is that summarization requires judgment about what is important — and importance is domain-dependent. What matters in a financial earnings call transcript (revenue guidance, margin trends, competitive positioning) is completely different from what matters in a patient discharge summary (medication changes, follow-up appointments, warning signs). Generic models apply generic importance signals, producing summaries that are grammatically correct but miss the specific information that makes a summary useful to a domain expert.

The Solution

Ertas enables teams to fine-tune summarization models on examples of expert-written summaries from their domain. By training on pairs of full documents and their corresponding expert summaries, the model learns not just how to compress text but what to prioritize for the specific audience and use case. Ertas Studio makes this process straightforward: teams upload JSONL datasets where each entry contains the source text and the target summary, and the training pipeline handles tokenization, context window management, and adapter optimization automatically.

The resulting model generates summaries that read like they were written by a domain expert — highlighting the metrics a financial analyst cares about, preserving the clinical details a physician needs, or retaining the legal citations a paralegal requires. Deployed through Ertas Cloud or locally via Ollama, the model can summarize documents on demand through an API, process document batches nightly, or power a real-time summarization feature in a user-facing application. As organizational priorities shift or new document types emerge, the model can be retrained in Ertas Studio with updated examples to keep summaries aligned with current needs.

Key Features

Example Workflow

A management consulting firm processes 200+ client deliverables weekly — industry analyses, competitive assessments, and strategic recommendations — each running 30-80 pages. Senior partners need 1-page executive summaries highlighting strategic insights, market sizing data, and recommended actions. The knowledge management team collects 5,000 document-summary pairs from past deliverables and uploads them to Ertas Vault. In Ertas Studio, they fine-tune a model that generates executive summaries matching partner expectations: structured into Strategic Context, Key Findings, and Recommended Actions sections. The model is deployed as an internal API integrated with the firm's document management system. When a consultant uploads a final deliverable, the system automatically generates a draft executive summary. Partners report that the AI-generated drafts capture 90% of the key points, reducing summary preparation time from 45 minutes to 5 minutes of review and editing.