A digital newspaper owns a large collection of articles and human-written associated article summaries. The summaries are used as headers for each article posted online, and the newspaper editors…

A neural topic model is used to identify underlying themes or topics within a collection of documents, not to generate coherent, human-readable summaries, and a bag-of-words representation discards word order which is crucial for summarization.

Projecting articles to a low-dimensional representation using averaged word2vec embeddings is suitable for tasks like document classification or similarity search, but it does not generate a new sequence of words that constitutes a summary.

Downsampling article words based on dropout probability is a technique used in word embedding training to manage frequent words, not a method for generating article summaries or creating a coherent textual output.

Sequence-to-sequence (seq2seq) models, typically employing recurrent neural networks (RNNs) in an encoder-decoder architecture, are the standard and most effective approach for neural text summarization because they can map an input sequence (the article) to a distinct output sequence (the summary) while capturing contextual dependencies. The encoder processes the entire input article, and the decoder then generates the summary word by word based on the encoded context.