ABSTRACT
The staggering amounts of content readily available to us via digital channels can often appear overwhelming. While much research has focused on aiding people at selecting relevant articles to read, only few approaches have been developed to assist readers in more efficiently reading an individual text. In this paper, we present HiText, a simple yet effective way of dynamically marking parts of a document in accordance with their salience. Rather than skimming a text by focusing on randomly chosen sentences, students and other readers can direct their attention to sentences determined to be important by our system. For this, we rely on a deep learning-based sentence ranking method. Our experiments show that this results in marked increases in user satisfaction and reading efficiency, as assessed using TOEFL-style reading comprehension tests.
- F. Ahmed, Y. Borodin, Y. Puzis, and I. V. Ramakrishnan. Why read if you can skim: Towards enabling faster screen reading. In Proc. W4A, pages 39:1--39:10, 2012. Google ScholarDigital Library
- P. L. Carrell. Facilitating esl reading by teaching text structure. TESOL Quarterly, 19(4):727--752, 1985.Google ScholarCross Ref
- J. Chen, N. Tandon, C. D. Hariman, and G. de Melo. WebBrain: Joint neural learning of large-scale commonsense knowledge. In Proc. ISWC, 2016.Google ScholarCross Ref
- E. H. Chi, L. Hong, M. Gumbrecht, and S. K. Card. Scent Highlights: Highlighting conceptually-related sentences during reading. In Proc. IUI, 2005. Google ScholarDigital Library
- J. Chung, Ç. Gülçehre, K. Cho, and Y. Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. CoRR, abs/1412.3555, 2014.Google Scholar
- D. Das and A. F. T. Martins. A survey on automatic text summarization. Technical report, Carnegie Mellon University, 2007.Google Scholar
- G. de Melo. Inducing conceptual embedding spaces from Wikipedia. In Proc. WWW 2017 (Cognitive Computing Track). ACM, 2017. Google ScholarDigital Library
- G. B. Duggan and S. J. Payne. Skim reading by satisficing: Evidence from eye tracking. In Proc. CHI 2011, pages 1141--1150. ACM, 2011. Google ScholarDigital Library
- G. Erkan and D. R. Radev. Lexrank: Graph-based lexical centrality as salience in text summarization. J. Artif. Int. Res., 22(1):457--479, Dec. 2004. Google ScholarDigital Library
- D. J. Gillick. The Elements of Automatic Summarization. PhD thesis, EECS Department, University of California, Berkeley, May 2011. Google ScholarDigital Library
- S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural Comput., 9(9):1735--1780, Nov. 1997.Google ScholarDigital Library
- N. Kalchbrenner and P. Blunsom. Recurrent continuous translation models. In Proc. EMNLP, pages 1700--1709. ACL, 2013.Google Scholar
- D. Kingery and R. Furuta. Skimming electronic newspaper headlines: a study of typeface, point size, screen resolution, and monitor size. Inf. Process. Manage., 33(5):685--696, 1997. Google ScholarDigital Library
- R. Kiros, Y. Zhu, R. Salakhutdinov, R. S. Zemel, A. Torralba, R. Urtasun, and S. Fidler. Skip-thought vectors. CoRR, abs/1506.06726, 2015.Google Scholar
- X. Long, C. Gan, and G. de Melo. Video captioning with multi-faceted attention. CoRR, abs/1612.00234, 2016.Google Scholar
- E. Loza Mencía, G. de Melo, and J. Nam. Medical concept embeddings via labeled background corpora. In Proc. LREC, Paris, France, 2016.Google Scholar
- H. P. Luhn. The automatic creation of literature abstracts. IBM Journal of Research and Development, 2(2):159--165, 1958. Google ScholarDigital Library
- M.-T. Luong, H. Pham, and C. D. Manning. Effective approaches to attention-based neural machine translation. In Proc. EMNLP, pages 1412--1421, 2015.Google ScholarCross Ref
- A. Nenkova and K. McKeown. A survey of text summarization techniques. In Mining Text Data. 2012.Google Scholar
- A. Nenkova and L. Vanderwende. The impact of frequency on summarization. Microsoft Research, Redmond, Washington, Tech. Rep. MSR-TR-2005-101, 2005.Google Scholar
- V. d. Paiva, D. Oliveira, S. Higuchi, A. Rademaker, and G. de Melo. Exploratory information extraction from a historical dictionary. In Proc. Workshop on Digital Humanities and e-Science at the 10th IEEE International Conference on e-Science, 2014. Google ScholarDigital Library
- E. Pitler and A. Nenkova. Revisiting readability: A unified framework for predicting text quality. In Proc. EMNLP, pages 186--195, 2008. Google ScholarDigital Library
- D. N. Rapp and P. Broek. Dynamic text comprehension: An integrative view of reading. Current Directions in Psychological Science, 14(5):276--279, 2005.Google ScholarCross Ref
- I. Sutskever, O. Vinyals, and Q. V. V. Le. Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems 27, pages 3104--3112. 2014. Google ScholarDigital Library
- M. Theobald, J. Siddharth, and A. Paepcke. Spotsigs: Robust and efficient near duplicate detection in large web collections. In Proc. SIGIR 2008, 2008. Google ScholarDigital Library
- W. M. Vagias. Likert-type scale response anchors. Clemson International Institute for Tourism & Research Development, 2006.Google Scholar
- O. Čulo and G. de Melo. Source-Path-Goal: Investigating the cross-linguistic potential of frame-semantic text analysis. it - Information Technology, 54, 2012.Google Scholar
- M. Walsh. The 'textual shift': examining the reading process with print, visual and multimodal texts. Australian Journal of Language and Literacy, 29(1):24--37, 2006.Google Scholar
- Y. Wang, Z. Ren, M. Theobald, M. Dylla, and G. de Melo. Summary generation for temporal extractions. In Proc. DEXA, 2016. Google ScholarDigital Library
- A. J. Wecker, J. Lanir, O. Mokryn, E. Minkov, and T. Kuflik. Semantize: Visualizing the sentiment of individual document. In Proc. AVI 2014, pages 385--386. ACM, 2014. Google ScholarDigital Library
- Q. Yang, R. J. Passonneau, and G. de Melo. PEAK: Pyramid evaluation via automated knowledge extraction. In Proc. AAAI. AAAI Press, 2016. Google ScholarDigital Library
- J. S. Yi. QnDReview: Read 100 CHI papers in 7 hours. In CHI '14 Extended Abstracts, pages 805--814. ACM, 2014. Google ScholarDigital Library
- K. X. Yong, K. Rajdev, T. J. Shakespeare, A. P. Leff, and S. J. Crutch. Facilitating text reading in posterior cortical atrophy. Neurology, 85(4):339--348, 2015.Google ScholarCross Ref
- Y. Zhu, R. Kiros, R. S. Zemel, R. Salakhutdinov, R. Urtasun, A. Torralba, and S. Fidler. Aligning books and movies: Towards story-like visual explanations by watching movies and reading books. CoRR, abs/1506.06724, 2015.Google Scholar
Index Terms
- HiText: Text Reading with Dynamic Salience Marking
Recommendations
How much do we understand when skim reading?
CHI EA '06: CHI '06 Extended Abstracts on Human Factors in Computing SystemsThe World Wide Web and other technological advances have meant rapid reading or "skimming" of text is increasingly common in our information-rich time-limited society. This study investigates the effectiveness of skimming as a strategy for understanding ...
Approaches to locating areas of interest related to questions in a document for non-visual readers
Assets '09: Proceedings of the 11th international ACM SIGACCESS conference on Computers and accessibilityThis poster describes an approach to creating a tool to aid blind, low-vision, dyslexic, and other non-visual readers in skimming a document to answer questions. The goal is to give them information similar to that obtained by a visual reader's skimming ...
Augmented Reality for Scene Text Recognition, Visualization and Reading to Assist Visually Impaired People
AbstractReading traffic signs while driving a car for visually impaired people and people with visual problems is a very difficult task for them. This task is encountered every day, sometimes incorrect reading of traffic signs can lead to very serious ...
Comments