Seq2Seq 모델로 요약하기

A seq2seq model maps one sequence of words to another. Feed it a long article and it produces a shorter sequence: your summary. 🔁

Seq2seq has two parts. The encoder reads and compresses the input, and the decoder writes the output one token at a time.

Because the decoder writes new tokens, seq2seq models are naturally abstractive. They paraphrase rather than copy, giving fluent summaries.

Models like BART, T5, and Pegasus are pre-trained seq2seq transformers built for summarization. You can use them without training your own.

Hugging Face wraps everything in a pipeline. One line loads a model and tokenizer ready to summarize.

Pass your text to the pipeline and read the result. The model returns a list with the generated summary_text.

Use max_length and min_length to bound the output size. Tighter limits force a shorter, punchier summary.

Each model has a maximum input size, often around 1024 tokens. Longer documents must be chunked or truncated first.

The decoder can pick the single best next token or explore several paths with beam search. Beams usually give smoother summaries.

Name a model to override the default. A summarization-tuned checkpoint like distilbart is smaller and faster for quick demos.

Seq2seq output is generated, so it can drift from the source. Always verify key facts before trusting an abstractive summary. ✅

Which part of a seq2seq model writes the summary tokens?

You ran a seq2seq summarizer with a Hugging Face pipeline, tuned its length, and learned to respect input limits and verify the output. 🎯