Recent neural network approaches to summarization are largely either selection-based
extraction or generation-based abstraction. In this work, we present a neural model for …

Much recent work suggests that incorporating syntax information from dependency trees can
improve task-specific transformer models. However, the effect of incorporating dependency …

Non-autoregressive generation (NAG) has recently attracted great attention due to its fast
inference speed. However, the generation quality of existing NAG models still lags behind …

Logs are one of the most valuable data sources for large-scale service (eg, social network,
search engine) maintenance. Log parsing serves as the the first step towards automated log …

Abstract Information extraction plays an important role for data transformation in business
cases. However, building extraction systems in actual cases face two challenges:(i) the …

We herein present a language-model-based evaluator for deletion-based sentence
compression and view this task as a series of deletion-and-evaluation operations using the …

Simplifying summaries of scholarly publications has been a popular method for conveying
scientific discoveries to a broader audience. While text summarization aims to shorten long …

Sentence compression reduces the length of text by removing non-essential content while
preserving important facts and grammaticality. Unsupervised objective driven methods for …

Abstractive text summarization that generates a summary by paraphrasing a long text
remains an open significant problem for natural language processing. In this paper, we …

Sentence compression is the task of compressing a long sentence into a short one by
deleting redundant words. In sequence-to-sequence (Seq2Seq) based models, the decoder …