Open Information Extraction (OpenIE) has been used in the pipelines of various NLP tasks.Unfortunately, there is no clear consensus on which models to use for which tasks.Muddying things further is the lack of comparisons that take differing training sets into account.In this paper, we present an application-focused empirical survey of neural OpenIE models, training sets, and benchmarks in an effort to help users choose the most suitable OpenIE systems for their applications.We find that the different assumptions made by different models and datasets have a statistically significant effect on performance, making it important to choose the most appropriate model for one's applications.We demonstrate the applicability of our recommendations on a downstream Complex QA application.

Open Information Extraction (OpenIE) is a traditional NLP task that extracts structured information from unstructured text to be used for other downstream applications.Traditionally, OpenIE focuses on extracting the surface forms of relations as they appear in the raw text, which we term extractive OpenIE.One of the main drawbacks of this approach is that implicit semantic relations (inferred relations) can not be extracted, compromising the performance of downstream applicationsIn this paper, we broaden the scope of OpenIE relations from merely the surface form of relations to include inferred relations, which we term abstractive OpenIE.This new task calls for the development of a new abstractive OpenIE training dataset and a baseline neural model that can extract those inferred relations.We also demonstrate the necessity for a new semantics-based metric for evaluating abstractive OpenIE extractions.Via a case study on Complex QA, we demonstrate the effectiveness of abstractive OpenIE. 1 1 Code and models are available at https://github.com/ kevinpei/AbstractiveOpenIE Sample SentenceTokyo, officially Tokyo Metropolis, is the capital city of Japan and one of its 47 prefectures.Extractive {Tokyo; is; the capital city of Japan} OpenIE Extractions {Tokyo; is; one of its 47 prefectures} {Tokyo; is; the capital city of Japan} Abstractive {Tokyo; is officially; Tokyo Metropolis} OpenIE Extractions {Tokyo; is; a prefecture} or {Tokyo; is; one of Japan's 47 prefectures}

Lecture slides covering many topics are becoming increasingly available online, but they are scattered, making it a challenge for anyone to instantly access all slides relevant to a learning context. To address this challenge, we propose to create links between those scattered slides to form a Web of Slides (WOS). Using the sequential nature of slides, we present preliminary results of studying how to automatically create a basic link based on similarity of slides as an initial step toward the vision of WOS. We also explore interesting future research directions using different link types and the unique features of slides.

We will demonstrate a prototype system WOSView built based on the vision of the Web of Slides(WOS), which aims to link all the lectures slides so as to facilitate navigation over all the slides. The links can be created at the slide level or at the level of phrases inside a slide, and many types of links can be created. The prototype system we built implements the most basic type of links, which link slides that have similar content and integrates lectures from four different MOOCs. WOSView also supports keyword search, which generates virtual links dynamically. We will demonstrate how the graphical interface of the WOSView enables students to flexibly navigate into slides from different courses and explore related slides using both static and dynamic links and solicit feedback from the community about the vision of WOS.