Learning to Skim Text

Recurrent Neural Networks are showing much promise in many sub-areas of natural language processing, ranging from document classification to machine translation to automatic question answering. Despite their promise, many recurrent models have to read the whole text word by word, making it slow to handle long documents. For example, it is difficult to use a recurrent network to read a book and answer questions about it. In this paper, we present an approach of reading text while skipping irrelevant information if needed. The underlying model is a recurrent network that learns how far to jump after reading a few words of the input text. We employ a standard policy gradient method to train the model to make discrete jumping decisions. In our benchmarks on four different tasks, including number prediction, sentiment analysis, news article classification and automatic Q\&A, our proposed model, a modified LSTM with jumping, is up to 6 times faster than the standard sequential LSTM, while maintaining the same or even better accuracy

Reading with new tools: An evaluation of Personal Digital Assistants as tools for reading course materials

Lightweight, palmtop devices such as personal digital assistants (PDAs) can now be used for reading electronic text, opening up their potential as learning tools. This paper reports a study that evaluated the use of PDAs for reading course materials by students on an Open University master's course. The research is grounded in activity theory, which provides a useful framework for examining how the introduction of a new tool changes an existing activity. Student perceptions of the possibilities and constraints of the PDA, as determined by questionnaires and interviews, reveal the impact the new tool had upon reading. The PDA constrained reading with limitations such as the small screen size, new requirements for navigating through the text and awkward methods for taking notes. These conditions made it difficult for students to skim‐read the text, to move back and forth within the document and to interact with the text as easily as they could with paper. Nevertheless, students welcomed the opportunity to have the course materials on a portable, lightweight device that could be used at any time and in any place. This made it easier to fit the reading activity around the various other activities in which students were involved In addition, the PDA was used in conjunction with existing tools, such as the printed version of the course materials and the desktop computer. Therefore, it was not seen to replace paper but rather to extend and complement it. The findings are discussed using concepts from activity theory to interpret how the new tool modified the reading activity

Neural Speed Reading with Structural-Jump-LSTM

Recurrent neural networks (RNNs) can model natural language by sequentially 'reading' input tokens and outputting a distributed representation of each token. Due to the sequential nature of RNNs, inference time is linearly dependent on the input length, and all inputs are read regardless of their importance. Efforts to speed up this inference, known as 'neural speed reading', either ignore or skim over part of the input. We present Structural-Jump-LSTM: the first neural speed reading model to both skip and jump text during inference. The model consists of a standard LSTM and two agents: one capable of skipping single words when reading, and one capable of exploiting punctuation structure (sub-sentence separators (,:), sentence end symbols (.!?), or end of text markers) to jump ahead after reading a word. A comprehensive experimental evaluation of our model against all five state-of-the-art neural reading models shows that Structural-Jump-LSTM achieves the best overall floating point operations (FLOP) reduction (hence is faster), while keeping the same accuracy or even improving it compared to a vanilla LSTM that reads the whole text.Comment: 10 page

Skim reading: an adaptive strategy for reading on the web

It has been suggested that readers spend a great deal of time skim reading on the Web and that if readers skim read they reduce their comprehension of what they have read. There have been a number of studies exploring skim reading, but relatively little exists on the skim reading of hypertext and Webpages. In the experiment documented here, we utilised eye tracking methodology to explore how readers skim read hypertext and how hyperlinks affect reading behaviour. The results show that the readers read faster when they were skim reading and comprehension was reduced. However, the presence of hyperlinks seemed to assist the readers in picking out important information when skim reading. We suggest that readers engage in an adaptive information foraging strategy where they attempt to minimise comprehension loss while maintaining a high reading speed. Readers use hyperlinks as markers to suggest important information and use them to read through the text in an efficient and effective way. This suggests that skim reading may not be as damaging to comprehension when reading hypertext, but it does mean that the words we choose to hyperlink become very important to comprehension for those skim reading text on the Web

Progression skills module 4: Getting ahead: personal learning and thinking skills

Progression skills modules are designed to support schools in delivering practical pupil workshops to help focus gifted and talented (G&T) or potential G&T pupils to aim high and achieve their best. This module develops the work of Progression skills module 2 to explore further independent study skills, including higher level reading skills, précis and critical thinking skills. Comprises: teacher notes, slide presentation, & pupil handouts

Synthesis of neural networks for spatio-temporal spike pattern recognition and processing

The advent of large scale neural computational platforms has highlighted the lack of algorithms for synthesis of neural structures to perform predefined cognitive tasks. The Neural Engineering Framework offers one such synthesis, but it is most effective for a spike rate representation of neural information, and it requires a large number of neurons to implement simple functions. We describe a neural network synthesis method that generates synaptic connectivity for neurons which process time-encoded neural signals, and which makes very sparse use of neurons. The method allows the user to specify, arbitrarily, neuronal characteristics such as axonal and dendritic delays, and synaptic transfer functions, and then solves for the optimal input-output relationship using computed dendritic weights. The method may be used for batch or online learning and has an extremely fast optimization process. We demonstrate its use in generating a network to recognize speech which is sparsely encoded as spike times.Comment: In submission to Frontiers in Neuromorphic Engineerin

The Correlation Between Prior Knowledge and Skimming Ability in Reading Comprehension of Second Semester Students of English Language Teaching Department at STAIN Jurai Siwo Metro

Prior knowledge is believed to be one of the factors related to students\u27 reading comprehension. In reading, a student brings their experience about the topic to the act of reading to ease them comprehend the text. In this case, selection of reading techniques is very crucial. Among reading technique which depends on prior knowledge is skimming. When students are to skim the text, their prior knowledge will help them catch the gist of the text easily because they are familiar with the topic of the text.Therefore, this research tried to investigate the correlation between prior knowledge and skimming ability in reading comprehension