What is masked document generation?
A task for seq2seq model pra-training that recovers a masked document to its original form.
Abstractive summarization aims to rewrite a long document to its shorter form, which is usually modeled as a sequence-to-sequence (Seq2Seq) learning problem. Seq2Seq Transformers are powerful models for this problem. Unfortunately, training large Seq2Seq Transformers on limited supervised summarization data is challenging. We, therefore, propose STEP (as shorthand for Sequence-to-Sequence Transformer Pre-training), which can be trained on large scale unlabeled documents. Specifically, STEP is pre-trained using three different tasks, namely sentence reordering, next sentence generation, and masked document generation. Experiments on two summarization datasets show that all three tasks can improve performance upon a heavily tuned large Seq2Seq Transformer which already includes a strong pre-trained encoder by a large margin. By using our best task to pre-train STEP, we outperform the best published abstractive model on CNN/DailyMail by 0.8 ROUGE-2 and New York Times by 2.4 ROUGE-2.
Large pre-trained language models BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4 improved the state-of-the-art of various natural language understanding (NLU) tasks such as question answering (e.g., SQuAD; BIBREF5), natural language inference (e.g., MNLI; BIBREF6) as well as text classification BIBREF7. These models (i.e., large LSTMs; BIBREF8 or Transformers; BIBREF9) are pre-trained on large scale unlabeled text with language modeling BIBREF0, BIBREF1, masked language modeling BIBREF2, BIBREF4 and permutation language modeling BIBREF3 objectives. In NLU tasks, pre-trained language models are mostly used as text encoders. Abstractive document summarization aims to rewrite a long document to its shorter form while still retaining its important information. Different from extractive document summarization that extacts important sentences, abstractive document summarization may paraphrase original sentences or delete contents from them. For more details on differences between abstractive and extractive document summary, we refer the interested readers to Nenkova:McKeown:2011 and Section SECREF2. This task is usually framed as a sequence-to-sequence learning problem BIBREF10, BIBREF11. In this paper, we adopt the sequence-to-sequence (seq2seq) Transformer BIBREF9, which has been demonstrated to be the state-of-the-art for seq2seq modeling BIBREF9, BIBREF12. Unfortunately, training large seq2seq Transformers on limited supervised summarization data is challenging BIBREF12 (refer to Section SECREF5). The seq2seq Transformer has an encoder and a decoder Transformer. Abstractive summarization requires both encoding of an input document and generation of a summary usually containing multiple sentences. As mentioned earlier, we can take advantage of recent pre-trained Transformer encoders for the document encoding part as in liu2019text. However, liu2019text leave the decoder randomly initialized. In this paper, we aim to pre-train both the encoder (i.e., the encoding part) and decoder (i.e., the generation part) of a seq2seq Transformer , which is able to improve abstractive summarization performance. Based on the above observations, we propose Step (as shorthand for Sequence-to-Sequence TransformEr Pre-training), which can be pre-trained on large scale unlabeled documents. Specifically, we design three tasks for seq2seq model pre-training, namely Sentence Reordering (SR), Next Sentence Generation (NSG), and Masked Document Generation (MDG). SR learns to recover a document with randomly shuffled sentences. NSG generates the next segment of a document based on its preceding segment. MDG recovers a masked document to its original form. After pre-trianing Step using the three tasks on unlabeled documents, we fine-tune it on supervised summarization datasets. We evaluate our methods on two summarization datasets (i.e., the CNN/DailyMail and the New York Times datasets). Experiments show that all three tasks we propose can improve upon a heavily tuned large seq2seq Transformer which already includes a strong pre-trained encoder by a large margin. Compared to the best published abstractive models, Step improves the ROUGE-2 by 0.8 on the CNN/DailyMail dataset and by 2.4 on the New York Times dataset using our best performing task for pre-training. Human experiments also show that Step can produce significantly better summaries in comparison with recent strong abstractive models.