A Generative Approach to Titling and Clustering Wikipedia Sections

WS 2020 · Anjalie Field, Sascha Rothe, Simon Baumgartner, Cong Yu, Abe Ittycheriah ·

We evaluate the performance of transformer encoders with various decoders for information organization through a new task: generation of section headings for Wikipedia articles. Our analysis shows that decoders containing attention mechanisms over the encoder output achieve high-scoring results by generating extractive text. In contrast, a decoder without attention better facilitates semantic encoding and can be used to generate section embeddings. We additionally introduce a new loss function, which further encourages the decoder to generate high-quality embeddings.

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Clustering

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Absolute Position Encodings • Adam • BPE • Dense Connections • Dropout • Label Smoothing • Layer Normalization • Linear Layer • Multi-Head Attention • Position-Wise Feed-Forward Layer • ReLU • Residual Connection • Scaled Dot-Product Attention • Softmax • Transformer

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A Generative Approach to Titling and Clustering Wikipedia Sections

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