Using Query Term Order for Result Summarisation

We report on two experiments performed to test the importance of Term Order in automatic summarisation. Experiment one was undertaken as part of DUC 2004 to which three systems were submitted, each with a different summarisation approach. The system that used document Term Order outperformed those that did not use Term Order in the ROUGE evaluation. Experiment two made use of human evaluations of search engine results, comparing our Query Term Order summaries with a simulation of current Google search engine result summaries in terms of summary quality. Our QTO system’s summaries aided users’ relevance judgements to a significantly greater extent than Google’s

Feature Selection for Summarising: The Sunderland DUC 2004 Experience

In this paper we describe our participation in task 1-very short single-document summaries in DUC 2004. The task chosen is related to our research project, which aims to produce abstracting summaries to improve search engine result summaries. DUC allowed us to produce summaries no longer than 75 characters, therefore we focused on feature selection to produce a set of key words as summaries instead of complete sentences. Three descriptions of our summarisers are given. Each of the summarisers performs very differently in the six ROUGE metrics. One of our summarisers which uses a simple algorithm to produce summaries without any supervised learning or complicated NLP technique performs surprisingly well among different ROUGE evaluations. Finally we give an analysis of ROUGE and participants’ results. ROUGE is an automatic evaluation of summaries package, which uses n-gram matching to calculate the overlapping between machine and human summaries, and indeed saves time for human evaluation. However, the different ROUGE metrics give different results and it is hard to judge which is the best for automatic summaries evaluation. Also it does not include complete sentences evaluation. Therefore we suggest some work needs to be done on ROUGE in the future to make it really effective

Can Automatic Abstracting Improve on Current Extracting Techniques in Aiding Users to Judge the Relevance of Pages in Search Engine Results?

Current search engines use sentence extraction techniques to produce snippet result summaries, which users may find less than ideal for determining the relevance of pages. Unlike extracting, abstracting programs analyse the context of documents and rewrite them into informative summaries. Our project aims to produce abstracting summaries which are coherent and easy to read thereby lessening users’ time in judging the relevance of pages. However, automatic abstracting technique has its domain restriction. For solving this problem we propose to employ text classification techniques. We propose a new approach to initially classify whole web documents into sixteen top level ODP categories by using machine learning and a Bayesian classifier. We then manually create sixteen templates for each category. The summarisation techniques we use include a natural language processing techniques to weight words and analyse lexical chains to identify salient phrases and place them into relevant template slots to produce summaries

Evaluating Web Search Result Summaries

The aim of our research is to produce and assess short summaries to aid users’ relevance judgements, for example for a search engine result page. In this paper we present our new metric for measuring summary quality based on representativeness and judgeability, and compare the summary quality of our system to that of Google. We discuss the basis for constructing our evaluation methodology in contrast to previous relevant open evaluations, arguing that the elements which make up an evaluation methodology: the tasks, data and metrics, are interdependent and the way in which they are combined is critical to the effectiveness of the methodology. The paper discusses the relationship between these three factors as implemented in our own work, as well as in SUMMAC/MUC/DUC

ZnO random laser diode arrays for stable single-mode operation at high power

2010-2011 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Oropharyngeal mucositis-specific quality-of-life measure in patients with cancer therapy.

published_or_final_versio

Distribution of raphespinal fibers in the mouse spinal cord

Background: Serotonergic raphespinal neurons and their fibers have been mapped in large mammals, but the non- serotonergic ones have not been studied, especially in the mouse. The present study aimed to investigate the termination pattern of fibers arising from the hindbrain raphe and reticular nuclei which also have serotonergic neurons by injecting the anterograde tracer BDA into them. Results: We found that raphespinal fibers terminate in both the dorsal and ventral horns in addition to lamina 10. There is a shift of the fibers in the ventral horn towards the dorsal and lateral part of the gray matter. Considerable variation in the termination pattern also exists between raphe nuclei with raphe magnus having more fibers terminating in the dorsal horn. Fibers from the adjacent gigantocellular reticular nucleus show similar termination pattern as those from the raphe nuclei with slight difference. Immunofluorescence staining showed that raphespinal fibers were heterogeneous and serotoninergic fibers were present in all laminae but mainly in laminae 1, 2, medial lamina 8, laminae 9 and 10. Surprisingly, immunofluorescence staining on clarified spinal cord tissue revealed that serotoninergic fibers formed bundles regularly in a short distance along the rostrocaudal axis in the medial part of the ventral horn and they extended towards the lateral motor neuron column area. Conclusion: Serotonergic and non-serotonergic fibers arising from the hindbrain raphe and reticular nuclei had similar termination pattern in the mouse spinal cord with subtle difference. The present study provides anatomical foundation for the multiple roles raphe and adjacent reticular nuclei play

Integrated Terahertz Graphene Modulator with 100% Modulation Depth

Terahertz (THz) frequency technology has many potential applications in nondestructive imaging, spectroscopic sensing, and high-bit-rate free-space communications, with an optical modulator being a key component. However, it has proved challenging to achieve high-speed modulation with a high modulation depth across a broad bandwidth of THz frequencies. Here, we demonstrate that a monolithically integrated graphene modulator can efficiently modulate the light intensity of the THz radiation from a THz quantum cascade laser with a 100% modulation depth for certain region of the pumping current, as a result of the strongly enhanced interaction between the laser field and the graphene enabled by this integration scheme. Moreover, the small area of the resulting device in comparison to existing THz modulators enables a faster modulation speed, greater than 100 MHz, which can be further improved through optimized designs of the laser cavity and modulator architectures. Furthermore, as the graphene absorption spectrum is broadband in nature, our integration scheme can be readily scaled to other wavelength regions, such as the mid-infrared, and applied to a broad range of other optoelectronic devices

Swirl Flow Bioreactor coupled with Cu-alginate beads: A system for the eradication of Coliform and Escherichia coli from biological effluents.

It is estimated that approximately 1.1 billion people globally drink unsafe water. We previously reported both a novel copper-alginate bead, which quickly reduces pathogen loading in waste streams and the incorporation of these beads into a novel swirl flow bioreactor (SFB), of low capital and running costs and of simple construction from commercially available plumbing pipes and fittings. The purpose of the present study was to trial this system for pathogen reduction in waste streams from an operating Dewats system in Hinjewadi, Pune, India and in both simulated and real waste streams in Seattle, Washington, USA. The trials in India, showed a complete inactivation of coliforms in the discharged effluent (Mean Log removal Value (MLRV) = 3.51), accompanied by a total inactivation of E. coli with a MLRV of 1.95. The secondary clarifier effluent also showed a 4.38 MLRV in viable coliforms during treatment. However, the system was slightly less effective in reducing E. coli viability, with a MLRV of 1.80. The trials in Seattle also demonstrated the efficacy of the system in the reduction of viable bacteria, with a LRV of 5.67 observed of viable Raoultella terrigena cells (100%)

Imperfect interface of Beclin1 coiled-coil domain regulates homodimer and heterodimer formation with Atg14L and UVRAG

Beclin 1 is a core component of the Class III Phosphatidylinositol 3-Kinase VPS34 complex. The coiled coil domain of Beclin 1 serves as an interaction platform for assembly of distinct Atg14L- and UVRAG-containing complexes to modulate VPS34 activity. Here we report the crystal structure of the coiled coil domain that forms an antiparallel dimer and is rendered metastable by a series of 'imperfect' a-d' pairings at its coiled coil interface. Atg14L and UVRAG promote the transition of metastable homodimeric Beclin 1 to heterodimeric Beclin1-Atg14L/UVRAG assembly. Beclin 1 mutants with their 'imperfect' a-d' pairings modified to enhance self-interaction, show distinctively altered interactions with Atg14L or UVRAG. These results suggest that specific utilization of the dimer interface and modulation of the homodimer–heterodimer transition by Beclin 1-interacting partners may underlie the molecular mechanism that controls the formation of various Beclin1–VPS34 subcomplexes to exert their effect on an array of VPS34-related activities, including autophagy