SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization

We study unsupervised multi-document summarization evaluation metrics, which require neither human-written reference summaries nor human annotations (e.g. preferences, ratings, etc.). We propose SUPERT, which rates the quality of a summary by measuring its semantic similarity with a pseudo reference summary, i.e. selected salient sentences from the source documents, using contextualized embeddings and soft token alignment techniques. Compared to the state-of-the-art unsupervised evaluation metrics, SUPERT correlates better with human ratings by 18-39%. Furthermore, we use SUPERT as rewards to guide a neural-based reinforcement learning summarizer, yielding favorable performance compared to the state-of-the-art unsupervised summarizers. All source code is available at https://github.com/yg211/acl20-ref-free-eval.Comment: ACL 202

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Survey of Query-based Text Summarization

Query-based text summarization is an important real world problem that requires to condense the prolix text data into a summary under the guidance of the query information provided by users. The topic has been studied for a long time and there are many existing interesting research related to query-based text summarization. Yet much of the work is not systematically surveyed. This survey aims at summarizing some interesting work in query-based text summarization methods as well as related generic text summarization methods. Not all taxonomies in this paper exist the related work to the best of our knowledge and some analysis will be presented

Recent Trends in Computational Intelligence

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications

Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses

Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem. Unfortunately, existing automatic evaluation metrics are biased and correlate very poorly with human judgements of response quality. Yet having an accurate automatic evaluation procedure is crucial for dialogue research, as it allows rapid prototyping and testing of new models with fewer expensive human evaluations. In response to this challenge, we formulate automatic dialogue evaluation as a learning problem. We present an evaluation model (ADEM) that learns to predict human-like scores to input responses, using a new dataset of human response scores. We show that the ADEM model's predictions correlate significantly, and at a level much higher than word-overlap metrics such as BLEU, with human judgements at both the utterance and system-level. We also show that ADEM can generalize to evaluating dialogue models unseen during training, an important step for automatic dialogue evaluation.Comment: ACL 201

Beyond Extractive: Advancing Abstractive Automatic Text Summarization in Norwegian with Transformers

Automatic summarization is a key area in natural language processing (NLP) and machine learning which attempts to generate informative summaries of articles and documents. Despite its evolution since the 1950s, research on automatically summarising Norwegian text has remained relatively underdeveloped. Though there have been some strides made in extractive systems, which generate summaries by selecting and condensing key phrases directly from the source material, the field of abstractive summarization remains unexplored for the Norwegian language. Abstractive summarization is distinct as it generates summaries incorporating new words and phrases not present in the original text. This Master's thesis revolves around one key question: Is it possible to create a machine learning system capable of performing abstractive summarization in Norwegian? To answer this question, we generate and release the first two Norwegian datasets for creating and evaluating Norwegian summarization models. One of these datasets is a web scrape of Store Norske Leksikon (SNL), and the other is a machine-translated version of CNN/Daily Mail. Using these datasets, we fine-tune two Norwegian T5 language models with 580M and 1.2B parameters to create summaries. To assess the quality of the models, we employed both automatic ROUGE scores and human evaluations on the generated summaries. In an effort to better understand the model's behaviour, we measure how a model generates summaries with various metrics, including our own novel contribution which we name "Match Ratio" which measures sentence similarities between summaries and articles based on Levenshtein distances. The top-performing models achieved ROUGE-1 scores of 35.07 and 34.02 on SNL and CNN/DM, respectively. In terms of human evaluation, the best model yielded an average score of 3.96/5.00 for SNL and 4.64/5.00 for CNN/Daily Mail across various criteria. Based on these results, we conclude that it is possible to perform abstractive summarization of Norwegian with high-quality summaries. With this research, we have laid a foundation that hopefully will facilitate future research, empowering others to build upon our findings and contribute further to the development of Norwegian summarization models