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XLNet: Generalized Autoregressive Pretraining for Language Understanding
XLNet is proposed, a generalized autoregressive pretraining method that enables learning bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order and overcomes the limitations of BERT thanks to its autore progressive formulation. Expand
The use of MMR, diversity-based reranking for reordering documents and producing summaries
This paper presents a method for combining query-relevance with information-novelty in the context of text retrieval and summarization, and preliminary results indicate some benefits for MMR diversity ranking in document retrieval and in single document summarization. Expand
Transformer-XL: Attentive Language Models beyond a Fixed-Length Context
This work proposes a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence, which consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Expand
Topic Detection and Tracking Pilot Study Final Report
Topic Detection and Tracking (TDT) is a DARPA-sponsored initiative to investigate the state of the art in finding and following new events in a stream of broadcast news stories. The TDT problemExpand
A Discriminative Graph-Based Parser for the Abstract Meaning Representation
The first approach to parse sentences into meaning representation, a semantic formalism for which a grow- ing set of annotated examples is available, is introduced, providing a strong baseline for improvement. Expand
A study of retrospective and on-line event detection
This paper applied hierarchical and non-hierarchical document clustering algorithms to a corpus of 15,836 stories, focusing on the exploitation of both content and temporal information, and found the resulting cluster hierarchies highly informative for retrospective detection of previously unidentified events. Expand
Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization
This work proposes a factor-based algorithm that is able to take time into account, and provides a fully Bayesian treatment to avoid tuning parameters and achieve automatic model complexity control. Expand
Summarizing text documents: sentence selection and evaluation metrics
An analysis of news-article summaries generated by sentence selection, using a normalized version of precision-recall curves with a baseline of random sentence selection to evaluate features and empirical results show the importance of corpus-dependent baseline summarization standards, compression ratios and carefully crafted long queries. Expand
Multi-Document Summarization By Sentence Extraction
This paper discusses a text extraction approach to multi-document summarization that builds on single-document summarization methods by using additional, available information about the document setExpand