AI-Powered Lecture Summarization System

Processing Lectures with Varied Audio Quality

The primary technical challenge in automatic lecture summarization is noise and accents in the audio track. Our AI note-taking system processes video, audio, and PDF slides using Whisper large-v3 (OpenAI) with the parameter word_timestamps=True. The model is robust to background noise and dialects; if necessary, we fine-tune it on the university's lecture corpus via model fine-tuning. For a quick draft, the base model can be used, but for academic texts, large-v3 yields a 15–20% accuracy improvement.

import whisper

model = whisper.load_model("large-v3")  # "base" for speed, "large-v3" for quality

result = model.transcribe(
    "lecture.mp4",
    language="en",
    verbose=False,
    word_timestamps=True,
)
transcript = result["text"]
segments = result["segments"]

For academic lectures with specialized terminology, large-v3 is significantly more accurate. Processing time on GPU: ~10 minutes of lecture in 30–60 seconds.

Why We Choose Whisper large-v3 for Academic Lectures

Transcription accuracy directly affects the quality of the summary. In tests on recordings from MIPT and HSE, large-v3 achieved a WER below 5%, while the base model erred on every tenth word. Research shows Whisper large-v3 achieves word error rate (WER) below 5% on academic lectures (Radford et al., 2023). We also apply post-processing: stop-word removal, term normalization, and punctuation restoration. This reduces the load on the LLM and speeds up final document generation. To improve accuracy on specialized subjects (medicine, law), we fine-tune the model on a corpus of 100–500 labeled lectures.

How We Build the System

We design the pipeline around your data and infrastructure. The standard stages:

1. Analysis — inspect source formats, choose optimal stack (Whisper, PyTorch, LangChain, Qdrant for vector search).
2. Design — develop processing scheme: audio → transcription → LLM prompt → structured summary → export.
3. Implementation — write code, configure models, integrate with LMS (Moodle, Canvas) via REST API.
4. Testing — run on 50+ real lectures, measure summary quality (ROUGE, BLEU).
5. Deployment — deploy on your GPU server or in the cloud (AWS SageMaker, Vertex AI).
6. Documentation and training — provide instructions for administrators and students.

How LLM Structures Notes and Avoids Hallucinations

After transcription, the text goes to an LLM (we use GPT-4 or Claude 3.5) with a detailed prompt. The prompt requires:

• Extract key points (max 3 per 10-minute segment).
• Group them into sections with examples and formulas.
• Generate 3–5 self-test questions.
• Do not add information absent from the original text.

For additional verification, we incorporate a RAG layer: before generating a section, the LLM retrieves the most semantically similar transcript fragments via vector search over embeddings (model intfloat/multilingual-e5-large). This reduces hallucination probability to a statistical margin (<0.5% in tests).

Timelines and How to Start

A typical turnkey project takes 2 to 6 weeks, depending on the number of sources (video only or slides + video) and the depth of customization (fine-tuning, RAG, custom prompts). Costs start at $5,000 and can save up to $20,000 per semester in manual transcription costs. Our system processes lectures 10x faster than manual note-taking.

Contact us to discuss your task. Get a free project evaluation and implementation consultation — we'll show how the system fits into your educational process.

What's Included in the Project

• Transcription pipeline setup (Whisper large-v3, fine-tuning if needed)
• LLM prompt engineering and RAG integration for accurate note structuring
• LMS REST API integration (Moodle, Blackboard, Canvas)
• User documentation and administrator training
• 1 month of post-deployment support

Comparison of Transcription Methods

Checklist for Pilot Project

• Collect 10–20 representative lectures in various formats (MP4, MP3, PDF).
• Identify the target LMS and integration method (API, file import).
• Choose the language and Whisper model (base or large-v3).
• Prepare LLM prompts for the desired summary style.
• Test on 3–5 lectures and adjust the pipeline.

Official Whisper documentation

NLP Development: Text Classification, NER, Embeddings, and Information Extraction

We often receive a task: process 50,000 support tickets — currently all manual. Dataset — 3,000 labeled examples, 12 categories, imbalance: one category occupies 40% of the sample, three at 1-2% each. Baseline accuracy — 78%. Sounds decent until you look at recall for rare classes: 0.31, 0.44, 0.28. These classes — complaints and churn threats — are most important to the business.

This is a typical NLP development project. The problem is not the algorithm but that accuracy is the wrong metric. Our experience across 30+ projects shows: we start by analyzing business metrics and only then choose the model.

Why accuracy is not the right metric for rare classes?

Accuracy ignores imbalance. If the "churn" class appears in 2% of cases, the model can predict "all good" and get 98% accuracy — but the business loses clients. Solution: F1 macro (averaged over all classes) or weighted F1. For NER — strict entity F1 (exact matches only). We guarantee: after choosing the correct metric, model quality becomes measurable and predictable.

Text Classification: From BERT to Distillation

BERT-like models are the standard for classification. ruBERT-base or ruBERT-large from DeepPavlov for Russian. multilingual-e5-large — for multiple languages in one pipeline. XLM-RoBERTa-large — a strong multilingual backbone.

Fine-tuning for classification: add a classification head on top of the [CLS] token, train for 3-5 epochs with lr=2e-5, weight decay=0.01. For imbalance — weighted CrossEntropyLoss or focal loss with gamma=2.0. Contact us — we will show a code snippet.

Imbalance case study. Dataset — 3,000 examples, imbalance 1:20. Solution: class_weight via sklearn + CrossEntropyLoss. Additionally — augmentation of rare classes via backtranslation (ru→en→ru through MarianMT). Recall for rare classes rose from 0.31 to 0.67 with a slight drop in accuracy (76%→74%). Full NLP development end-to-end took 3 weeks.

Distillation for production. BERT-large gives F1 0.89, but inference on CPU — 180ms. Distillation into DistilBERT or ruBERT-tiny2 reduces latency to 25ms with F1 0.84. Export to ONNX Runtime provides an additional 1.5-2x speedup. DistilBERT achieves 7x lower latency than BERT-large with only a 5% drop in macro F1 – a typical production trade-off.

How to choose between BERT and LLM for your task?

For most classification and extraction tasks, BERT-sized models offer the best trade-off between cost and performance. Shift to LLMs only when the task demands generation, complex reasoning, or zero-shot generalization.

NER: Named Entity Recognition

NER — extracting persons, organizations, locations, dates, amounts, document numbers. For general categories (PER, ORG, LOC), pre-trained models work well. For specialized ones (medical terms, legal concepts) — fine-tuning is needed.

Data annotation. The main cost of an NER project. For a quality model — 500-2,000 labeled sentences per entity type. Tools: Label Studio (open source) or Prodigy (by spaCy creators). IOB2 format — standard.

Architecture. Token classification on top of BERT: each token gets a label (B-PER, I-PER, O). spaCy 3.x with transformer pipeline — a convenient production choice.

Nested entities. Standard IOB models cannot handle nested entities (organization inside an address). For such tasks — span-based NER: SpanBERT or SpERT. More complex but correct.

Post-processing is mandatory. The model predicts tokens — normalized entities are needed. Date — dateparser. Amounts — regex + validation. Names — deduplication via rapidfuzz. Included in our standard delivery.

Sentiment Analysis and Opinion Mining

Binary classification positive/negative works out of the box with BERT. Complexity — aspect-based sentiment analysis (ABSA): "the restaurant has good food but terrible service." For ABSA: aspect extraction (NER) + sentiment per aspect. Joint models BERT-for-ABSA — quality on Russian data is lower due to dataset scarcity. RuSentiment, SentiRuEval — main resources.

For production with simple positive/negative/neutral: distil models are enough. Three classes, balanced dataset, 2,000+ examples — F1 macro 0.82-0.87 in 1-2 days.

Text Summarization

Extractive summarization (select sentences) — TextRank or BM25 without training. Fast, no hallucinations. Good for long documents.

Abstractive (generates new text) — seq2seq: mT5, mBART, FRED-T5, ruT5-large. For production via LLM API (GPT-4, Claude) — often the best cost/quality/speed trade-off.

Embeddings: Vector Representations of Text

Embeddings are the foundation of semantic search, deduplication, clustering, RAG. Quality critically affects downstream tasks.

Models. E5-large-v2, BGE-M3, multilingual-e5-large — strong multilingual embedders. sentence-transformers/paraphrase-multilingual-mpnet-base-v2 — fast option. For Russian: ru-en-RoSBERTa (Skoltech) performs well on semantic textual similarity.

Embedding quality evaluation uses the MTEB benchmark as standard. But top results on MTEB don't guarantee success on a domain dataset — we build domain-specific eval.

Fine-tuning embeddings. If standard models don't give the required Recall@k — contrastive learning on domain pairs with MultipleNegativesRankingLoss. How to perform this for domain data:

1. Collect 500–2,000 semantically similar pairs from your domain.
2. Apply MultipleNegativesRankingLoss with a batch size of 32–64.
3. Train for 1–3 epochs using AdamW (lr=2e-5).
4. Evaluate Recall@k on a held-out domain test set.

This approach yields a 5–15% improvement in Recall@k in practice.

Dimensionality and storage. E5-large: 1024 dim, float32 — 4KB per vector. For 10M documents — 40GB. Quantization int8 reduces to 10GB. FAISS IVF_PQ — more compact but with losses. Included in our deployment recommendations.

Information Extraction

Structured extraction is a frequent task. Examples: key contract terms, technical characteristics, dates and amounts from invoices.

1. Regex + rule-based. For INN, OGRN, amounts, dates — more reliable than neural networks. No data required.
2. NER + post-processing. For variable formats.
3. LLM with structured output. GPT‑4 / Claude with JSON schema — for complex documents. Cost: minimal per document. For 10k+ documents/day — we calculate the economics.

We guarantee a hybrid: regex/NER for typical fields + LLM for edge cases. Our guarantee is backed by years of production experience and more than 30 projects.

Work Stages

Prototype on existing data — 1-3 weeks. Production system with CI/CD — 1.5–2.5 months. Cost is calculated individually — get a consultation for a project estimate.

What's Included

• Model and pipeline architecture documentation
• Access to the model via REST API (FastAPI + ONNX)
• Client team training (2-hour webinar + Q&A)
• Accuracy guarantee on the agreed test set
• Months of post-delivery support (bug fixes, adaptation to new data)

Our Experience

Years of NLP projects from classification to RAG systems. The team includes ML engineers experienced with Hugging Face, spaCy, LangChain, MLOps. We use vLLM, Kubeflow, Weights & Biases — a production stack, not toys. Contact us to evaluate your NLP project within two days — request a free consultation on your text processing pipeline.