Neural text summarization has shown great potential in recent years. However, current state-of-the-art summarization models are limited by their maximum input length, posing a challenge to summarizing longer texts comprehensively. As part of a layered summarization architecture, we introduce PureText, a simple yet effective pre-processing layer that removes low- quality sentences in articles to improve existing summarization models. When evaluated on popular datasets like WikiHow and Reddit TIFU, we show up to 3.84 and 8.57 point ROUGE-1 absolute improvement on the full test set and the long article subset, respectively, for state-of-the-art summarization models such as BertSum and BART. Our approach provides downstream models with higher-quality sentences for summarization, improving overall model performance, especially on long text articles.

Aswarth Abhilash Dara, Josef van Genabith, Qun Liu, John Judge, Antonio Toral. Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers. 2014.

This paper describes the development of QuestionBank, a corpus of 4000 parse-annotated questions for (i) use in training parsers employed in QA, and (ii) evaluation of question parsing. We present a series of experiments to investigate the effectiveness of QuestionBank as both an exclusive and supplementary training resource for a state-of-the-art parser in parsing both question and non-question test sets. We introduce a new method for recovering empty nodes and their antecedents (capturing long distance dependencies) from parser output in CFG trees using LFG f-structure reentrancies. Our main findings are (i) using QuestionBank training data improves parser performance to 89.75% labelled bracketing f-score, an increase of almost 11% over the baseline; (ii) back-testing experiments on non-question data (Penn-II WSJ Section 23) shows that the retrained parser does not suffer a performance drop on non-question material; (iii) ablation experiments show that the size of training material provided by QuestionBank is sufficient to achieve optimal results; (iv) our method for recovering empty nodes captures long distance dependencies in questions from the ATIS corpus with high precision (96.82%) and low recall (39.38%). In summary, QuestionBank provides a useful new resource in parser-based QA research.

We address issues with the performance of IEEE 802.11, when used in the adhoc mode, in the presence of hidden terminals. We present results illustrating the strong dependence of channel capture behavior on the SNR observed on contending hidden connections. Experimental work has illustrated that in a hidden terminal scenario, the connection having the strongest SNR is able to capture the channel, despite the use of the RTS-CTS-DATA-ACK 4-way handshake designed to alleviate this problem. Our results indicate that the near-far SNR problem may have a significant effect on the performance of an adhoc 802.11 network.

This paper presents a new model for aggregate hypertext transfer protocol (HTTP) request rate. Specifically the model describes the marginal distribution of aggregate HTTP request rate at the second time scale for the aggregate Web traffic generated by a large number of users accessing the Web. The examination of three independent traces of Web traffic shows that the marginal distribution of HTTP request rate is well modelled by the Polya-Aeppli probability distribution. The Polya-Aeppli result is based on observations that the marginal distribution of active users is Poisson and that the distribution of the number of requests generated by an active user is approximately geometric. The Polya-Aeppli result has immediate application in the estimation of peak HTTP request rates from known mean and variance. The result also highlights a discrepancy between artificial Web traffic workloads used for cache benchmarking based on the Poisson assumption and actual Web traffic.