atulkum/pointer_summarizer
pytorch implementation of "Get To The Point: Summarization with Pointer-Generator Networks"
Implements a hybrid seq2seq-attention architecture that combines copy mechanisms (pointer networks) with vocabulary generation, enabling the model to both reproduce source text and generate novel words. Includes optional coverage loss to penalize repetitive attention patterns, improving coherence on abstractive summarization tasks. Trainable end-to-end on CNN/DailyMail datasets with beam search decoding and ROUGE evaluation metrics built in.
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915
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Language
Python
License
Apache-2.0
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Last pushed
Jan 23, 2023
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