AI0-001 AI Infrastructure and Technologies Practice Question
A media company wants to automatically generate concise summaries of lengthy earnings-call transcripts. The transcripts average 45 minutes of speech and contain domain-specific financial terminology. The team needs a solution that captures long-range dependencies and produces fluent, abstractive summaries without training a model from scratch. Which approach is most appropriate?
⚠ Common exam trap
The trap here is assuming extractive methods or topic models can satisfy a requirement for fluent abstractive summaries, when only generative sequence-to-sequence models actually paraphrase and synthesize content.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Fine-tune a pretrained encoder-decoder transformer (e.g., BART or T5) on the earnings-call corpus using abstractive summarization objectives.
Abstractive summarization of long, domain-specific transcripts is best served by fine-tuning a pretrained encoder-decoder transformer. Models like BART and T5 are pretrained on vast text corpora, giving them strong language understanding, and their architecture handles long-range dependencies through self-attention. Fine-tuning on earnings-call data adapts them to financial terminology and summary style without the prohibitive cost of training from scratch. This balances quality, fluency, and practicality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Fine-tune a pretrained encoder-decoder transformer (e.g., BART or T5) on the earnings-call corpus using abstractive summarization objectives.
Why this is correct
Encoder-decoder transformers pretrained on large text corpora already understand language structure and can be fine-tuned on domain transcripts to learn financial terminology and summary style. BART and T5 are specifically designed for sequence-to-sequence tasks like abstractive summarization, and their self-attention captures long-range dependencies across thousands of tokens. Fine-tuning avoids training from scratch while adapting to the domain, making this the most effective and efficient choice.
- ✗
Apply latent Dirichlet allocation (LDA) topic modeling to identify key themes, then generate summaries from the top topics.
Why it's wrong here
LDA is an unsupervised topic modeling technique that discovers latent themes in a document collection. It does not produce coherent, fluent summaries; it outputs topic-word distributions and document-topic mixtures. Converting those into readable summaries would require additional generative steps and would likely miss nuanced financial details. LDA also ignores word order and long-range dependencies, making it unsuitable for abstractive summarization of earnings calls.
- ✗
Use an extractive summarization algorithm like TextRank to select the most important sentences from each transcript.
Why it's wrong here
TextRank builds a graph of sentences and ranks them by similarity, selecting top sentences verbatim. While computationally cheap, it produces extractive summaries that copy original phrasing, which may be verbose and lack the fluency and conciseness of abstractive summaries. It also cannot paraphrase or synthesize information across distant parts of a 45-minute transcript, and it does not leverage pretrained language understanding, so domain adaptation is minimal.
- ✗
Train a large LSTM-based sequence-to-sequence model from scratch on the transcripts with attention.
Why it's wrong here
Training from scratch requires massive amounts of labeled data and compute, which the media company likely lacks for a single domain. LSTMs also struggle with very long sequences due to vanishing gradients and limited effective context, making it hard to capture dependencies across a 45-minute transcript. This approach is both resource-intensive and technically inferior to fine-tuning a pretrained transformer for long-range abstractive summarization.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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