An Analytical Study of Algorithmic and Expert Summaries of Legal Cases

Automatic summarization of legal case documents is an important and challenging problem, where algorithms attempt to generate summaries that match well with expert-generated summaries. This work takes the first step in analyzing expert-generated summaries and algorithmic summaries of legal case documents. We try to uncover how law experts write summaries for a legal document, how various generic as well as domain-specific extractive algorithms generate summaries, and how the expert summaries vary from the algorithmic summaries. We also analyze which important sentences of a legal case document are missed by most algorithms while generating summaries, in terms of the rhetorical roles of the sentences and the positions of the sentences in the legal document.

Video-Based Analysis and Reporting of Riding Behavior in Cyclocross Segments

Video-based trajectory analysis might be rather well discussed in sports, such as soccer or basketball, but in cycling, this is far less common. In this paper, a video processing pipeline to extract riding lines in cyclocross races is presented. The pipeline consists of a stepwise analysis process to extract riding behavior from a region (i.e., the fence) in a video camera feed. In the first step, the riders are identified by an Alphapose skeleton detector and tracked with a spatiotemporally aware pose tracker. Next, each detected pose is enriched with additional meta-information, such as rider modus (e.g., sitting on the saddle or standing on the pedals) and detected team (based on the worn jerseys). Finally, a post-processor brings all the information together and proposes ride lines with meta-information for the riders in the fence. The presented methodology can provide interesting insights, such as intra-athlete ride line clustering, anomaly detection, and detailed breakdowns of riding and running durations within the segment. Such detailed rider info can be very valuable for performance analysis, storytelling, and automatic summarization.

SA-HAVE: A Self-Attention based Hierarchical VAEs Network for Abstractive Summarization

The abstractive automatic summarization task is to summarize the main content of the article with short sentences, which is an important research direction in natural language generation. Most abstractive summarization models are based on sequence-to-sequence neural networks. Specifically, they encode input text sequences by Bi-directional Long Short-Term Memory (bi-LSTM), and decode summaries word-by-word by LSTM. However, existing models usually did not consider both the self-attention dependence during the encoding process using bi-LSTM, and deep potential sentence structure information for the decoding process. To tackle these limitations, we propose a Self-Attention based word embedding and Hierarchical Variational AutoEncoders (SA-HVAE) model. The model first introduces self-attention into LSTM to alleviate information decay of encoding, and accomplish summarization with deep structure information inference through hierarchical VAEs. The experimental results on the Gigaword and CNN/Daily Mail datasets validate the superior performance of SA-HVAE, and our model has a significant improvement over the baseline model.

Hybridization of DBN with SVM and its Impact on Performance in Multi-Document Summarization

Data available from web based sources has grown tremendously with growth of the internet. Users interested in information from such sources often use a search engine to obtain the data which they edit for presentation to their audience. This process can be tedious especially when it involves the generation of a summary. One way to ease the process is by automation of the summary generation process. Efforts by researchers towards automatic summarization have yielded several approaches among them machine learning. Thus, recommendations have been made on combining the algorithms with different strengths, also called hybridization, in order to enhance their performance. Therefore, this research sought to establish the impact of hybridization of Deep Belief Network (DBN) with Support Vector Machine (SVM) on precision, recall, accuracy and F-measure when used in the case of query oriented multi-document summarization. The experiments were carried out using data from National Institute of Standards and Technology (NIST), Document Understanding Conference (DUC) 2006. The data was split into training and test data and used appropriately in DBN, SVM, SVM-DBN hybrid and DBN-SVM hybrid. Results indicated that the hybridized algorithm has better precision, accuracy and F-measure as compared to DBN. Pre-classification hybridization of DBN with SVM (SVM-DBN) gives the best results. This research implies that use of DBN and SVM hybrid algorithms would enhance query oriented multi-document summarization.

A Case Study on User Evaluation of Scientific Publication Summarization by Japanese Students

Summaries of scientific publications enable readers to gain an overview of a large number of studies, but users’ preferences have not yet been explored. In this paper, we conduct two user studies (i.e., short- and long-term studies) where Japanese university students read summaries of English research articles that were either manually written or automatically generated using text summarization and/or machine translation. In the short-term experiment, subjects compared and evaluated the two types of summaries of the same article. We analyze the characteristics in the generated summaries that readers regard as important, such as content richness and simplicity. The experimental results show that subjects are mainly judged based on four criteria, including content richness, simplicity, fluency, and format. In the long-term experiment, subjects read 50 summaries and answered whether they would like to read the original papers after reading the summaries. We discuss the characteristics in the summaries that readers tend to use to determine whether to read the papers, such as topic, methods, and results. The comments from subjects indicate that specific components of scientific publications, including research topics and methods, are important to judge whether to read or not. Our study provides insights to enhance the effectiveness of automatic summarization of scientific publications.

Automatic Summarization on Restaurants Opinion Mining

Innovation has been a fundamental piece of our lives. When going bent another cafe/restaurants or bistro, individuals normally use sites or applications to question close by spots and afterward select one hooked in to normal rating. Notwithstanding, the traditional rating is now and again insufficient to foresee the character of the café as individuals have alternate points of view and wishes while assessing an eatery. During this paper, an overview framework for sentiments in eatery audits is proposed. The framework may be a useful device for clients during a hurry to assist them improve decisions about the nature of a restaurant while saving their time. This is often finished via naturally and rapidly furnishing the clients with a synopsis of the sentiments within the café's/restaurants surveys. The proposed synopsis framework has been actualized during a versatile area based application with KNN Algorithm and Multi keyword search it accomplished a high convenience score.

A Joint Summarization and Pre-Trained Model for Review-Based Recommendation

Currently, reviews on the Internet contain abundant information about users and products, and this information is of great value to recommendation systems. As a result, review-based recommendations have begun to show their effectiveness and research value. Due to the accumulation of a large number of reviews, it has become very important to extract useful information from reviews. Automatic summarization can capture important information from a set of documents and present it in the form of a brief summary. Therefore, integrating automatic summarization into recommendation systems is a potential approach for solving this problem. Based on this idea, we propose a joint summarization and pre-trained recommendation model for review-based rate prediction. Through automatic summarization and a pre-trained language model, the overall recommendation model learns a fine-grained summary representation of the key content as well as the relationships between words and sentences in each review. The review summary representations of users and items are finally incorporated into a neural collaborative filtering (CF) framework with interactive attention mechanisms to predict the rating scores. We perform experiments on the Amazon dataset and compare our method with several competitive baselines. Experimental results show that the performance of the proposed model is obviously better than that of the baselines. Relative to the current best results, the average improvements obtained on four sub-datasets randomly selected from the Amazon dataset are approximately 3.29%.