Papers › Abstractive Text Classification Using Sequence-to-convolution Neural Networks
Abstractive Text Classification Using Sequence-to-convolution Neural Networks
Taehoon Kim, Jihoon Yang
We propose a new deep neural network model and its training scheme for text classification. Our model Sequence-to-convolution Neural Networks(Seq2CNN) consists of two blocks: Sequential Block that summarizes input texts and Convolution Block that receives summary of input and classifies it to a label. Seq2CNN is trained end-to-end to classify various-length texts without preprocessing inputs into fixed length. We also present Gradual Weight Shift(GWS) method that stabilizes training. GWS is applied to our model's loss function. We compared our model with word-based TextCNN trained with different data preprocessing methods. We obtained significant improvement in classification accuracy over word-based TextCNN without any ensemble or data augmentation.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text Classification | AG News | Seq2CNN with GWS(50) | Error | 9.64 | #20 of 24 | Archive leaderboard | report |
| Text Classification | DBpedia | Seq2CNN(50) | Error | 2.77 | #21 of 21 | Archive leaderboard | report |
| Text Classification | Yahoo! Answers | Seq2CNN(50) | Accuracy | 55.39 | #10 of 10 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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