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BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization
This work presents a novel dataset, BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries, which has the following properties: i) summaries contain a richer discourse structure with more recurring entities, ii) salient content is evenly distributed in the input, and iii) lesser and shorter extractive fragments are present in the summaries. Expand
Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards
This work proposes a reinforcement learning (RL) approach for keyphrase generation, with an adaptive reward function that encourages a model to generate both sufficient and accurate keyphrases, and introduces a new evaluation method that incorporates name variations of the ground-truth keyphRases using the Wikipedia knowledge base. Expand
An Entity-Driven Framework for Abstractive Summarization
SENECA is introduced, a novel System for ENtity-drivEn Coherent Abstractive summarization framework that leverages entity information to generate informative and coherent abstracts and significantly outperforms previous state-of-the-art based on ROUGE and proposed coherence measures on New York Times and CNN/Daily Mail datasets. Expand
In Plain Sight: Media Bias Through the Lens of Factual Reporting
There is evidence that informational bias appears in news articles more frequently than lexical bias, and a baseline model for informational bias prediction is presented by fine-tuning BERT on labeled data, indicating the challenges of the task and future directions. Expand
Discourse as a Function of Event: Profiling Discourse Structure in News Articles around the Main Event
This work applies an existing theory of functional discourse structure for news articles that revolves around the main event and creates a human-annotated corpus of 802 documents spanning over four domains and three media sources to enable computational modeling of news structures. Expand
Taking control amidst the chaos: Emotion regulation during the COVID-19 pandemic☆
This commentary discusses the implications of COVID-19 for maintaining one's psychological well-being and employment security, and also managing family and work responsibilities and offers several suggestions for future scholarly investigation into how this pandemic impacts vocational behavior. Expand
Joint vs. Separate Crowdsourcing Contests
In a crowdsourcing contest, innovation is outsourced by a firm to an open crowd that compete in generating innovative solutions. Given that the projects typically consist of multiple attributes, howExpand
Sentence-Level Content Planning and Style Specification for Neural Text Generation
This work presents an end-to-end trained two-step generation model, where a sentence-level content planner first decides on the keyphrases to cover as well as a desired language style, followed by a surface realization decoder that generates relevant and coherent text. Expand
Forecasting stock price volatility: New evidence from the GARCH-MIDAS model
Abstract This paper introduces a combination of asymmetry and extreme volatility effects in order to build superior extensions of the GARCH-MIDAS model for modeling and forecasting the stockExpand
Neural Conversation Recommendation with Online Interaction Modeling
A novel framework to automatically recommend conversations to users based on their prior conversation behaviors is presented, built on neural collaborative filtering and incorporates graph-structured networks to identify salient characteristics from interleaving user interactions. Expand