Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization

Generating a text abstract from a set of documents remains a challenging task. The neural encoder-decoder framework has recently been exploited to summarize single documents, but its success can in part be attributed to the availability of large parallel data automatically acquired from the Web. In contrast, parallel data for multi-document summarization are scarce and costly to obtain. There is a pressing need to adapt an encoder-decoder model trained on single-document summarization data to work with multiple-document input. In this paper, we present an initial investigation into a novel adaptation method. It exploits the maximal marginal relevance method to select representative sentences from multi-document input, and leverages an abstractive encoder-decoder model to fuse disparate sentences to an abstractive summary. The adaptation method is robust and itself requires no training data. Our system compares favorably to state-of-the-art extractive and abstractive approaches judged by automatic metrics and human assessors.Comment: 11 page

Toward Extractive Summarization of Online Forum Discussions via Hierarchical Attention Networks

Forum threads are lengthy and rich in content. Concise thread summaries will benefit both newcomers seeking information and those who participate in the discussion. Few studies, however, have examined the task of forum thread summarization. In this work we make the first attempt to adapt the hierarchical attention networks for thread summarization. The model draws on the recent development of neural attention mechanisms to build sentence and thread representations and use them for summarization. Our results indicate that the proposed approach can outperform a range of competitive baselines. Further, a redundancy removal step is crucial for achieving outstanding results.Comment: 5 page

Toward Extractive Summarization of Online Forum Discussions via Hierarchical Attention Networks

Forum threads are lengthy and rich in content. Concise thread summaries will benefit both newcomers seeking information and those who participate in the discussion. Few studies, however, have examined the task of forum thread summarization. In this work we make the first attempt to adapt the hierarchical attention networks for thread summarization. The model draws on the recent development of neural attention mechanisms to build sentence and thread representations and use them for summarization. Our results indicate that the proposed approach can outperform a range of competitive baselines. Further, a redundancy removal step is crucial for achieving outstanding results.Comment: 5 page

A Novel ILP Framework for Summarizing Content with High Lexical Variety

Summarizing content contributed by individuals can be challenging, because people make different lexical choices even when describing the same events. However, there remains a significant need to summarize such content. Examples include the student responses to post-class reflective questions, product reviews, and news articles published by different news agencies related to the same events. High lexical diversity of these documents hinders the system's ability to effectively identify salient content and reduce summary redundancy. In this paper, we overcome this issue by introducing an integer linear programming-based summarization framework. It incorporates a low-rank approximation to the sentence-word co-occurrence matrix to intrinsically group semantically-similar lexical items. We conduct extensive experiments on datasets of student responses, product reviews, and news documents. Our approach compares favorably to a number of extractive baselines as well as a neural abstractive summarization system. The paper finally sheds light on when and why the proposed framework is effective at summarizing content with high lexical variety.Comment: Accepted for publication in the journal of Natural Language Engineering, 201

A Review on Human-Computer Interaction and Intelligent Robots

In the field of artificial intelligence, human–computer interaction (HCI) technology and its related intelligent robot technologies are essential and interesting contents of research. From the perspective of software algorithm and hardware system, these above-mentioned technologies study and try to build a natural HCI environment. The purpose of this research is to provide an overview of HCI and intelligent robots. This research highlights the existing technologies of listening, speaking, reading, writing, and other senses, which are widely used in human interaction. Based on these same technologies, this research introduces some intelligent robot systems and platforms. This paper also forecasts some vital challenges of researching HCI and intelligent robots. The authors hope that this work will help researchers in the field to acquire the necessary information and technologies to further conduct more advanced research

Abstractive multi-document summarization - paraphrasing and compressing with neural networks

This thesis presents studies in neural text summarization for single and multiple documents.The focus is on using sentence paraphrasing and compression for generating fluent summaries, especially in multi-document summarization where there is data paucity. A novel solution is to use transfer-learning from downstream tasks with an abundance of data. For this purpose, we pre-train three models for each of extractive summarization, paraphrase generation and sentence compression. We find that summarization datasets – CNN/DM and NEWSROOM – contain a number of noisy samples. Hence, we present a method for automatically filtering out this noise. We combine the representational power of the GRU-RNN and TRANSFORMER encoders in our paraphrase generation model. In training our sentence compression model, we investigate the impact of using different early-stopping criteria, such as embedding-based cosine similarity and F1. We utilize the pre-trained models (ours, GPT2 and T5) in different settings for single and multi-document summarization.SGS Tuition Award Alberta Innovates Technology Futures (AITF