Abstract Meaning Representation for Multi-Document Summarization

Generating an abstract from a collection of documents is a desirable capability for many real-world applications. However, abstractive approaches to multi-document summarization have not been thoroughly investigated. This paper studies the feasibility of using Abstract Meaning Representation (AMR), a semantic representation of natural language grounded in linguistic theory, as a form of content representation. Our approach condenses source documents to a set of summary graphs following the AMR formalism. The summary graphs are then transformed to a set of summary sentences in a surface realization step. The framework is fully data-driven and flexible. Each component can be optimized independently using small-scale, in-domain training data. We perform experiments on benchmark summarization datasets and report promising results. We also describe opportunities and challenges for advancing this line of research.Comment: 13 page

Unsupervised Multi-document Summarization with Holistic Inference

Multi-document summarization aims to obtain core information from a collection of documents written on the same topic. This paper proposes a new holistic framework for unsupervised multi-document extractive summarization. Our method incorporates the holistic beam search inference method associated with the holistic measurements, named Subset Representative Index (SRI). SRI balances the importance and diversity of a subset of sentences from the source documents and can be calculated in unsupervised and adaptive manners. To demonstrate the effectiveness of our method, we conduct extensive experiments on both small and large-scale multi-document summarization datasets under both unsupervised and adaptive settings. The proposed method outperforms strong baselines by a significant margin, as indicated by the resulting ROUGE scores and diversity measures. Our findings also suggest that diversity is essential for improving multi-document summary performance.Comment: Findings of IJCNLP-AACL 202

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

Methods of sentence extraction, abstraction and ordering for automatic text summarization

In this thesis, we have developed several techniques for tackling both the extractive and abstractive text summarization tasks. We implement a rank based extractive sentence selection algorithm. For ensuring a pure sentence abstraction, we propose several novel sentence abstraction techniques which jointly perform sentence compression, fusion, and paraphrasing at the sentence level. We also model abstractive compression generation as a sequence-to-sequence (seq2seq) problem using an encoder-decoder framework. Furthermore, we applied our sentence abstraction techniques to the multi-document abstractive text summarization. We also propose a greedy sentence ordering algorithm to maintain the summary coherence for increasing the readability. We introduce an optimal solution to the summary length limit problem. Our experiments demonstrate that the methods bring significant improvements over the state-of-the-art methods. At the end of this thesis, we also introduced a new concept called "Reader Aware Summary" which can generate summaries for some critical readers (e.g. Non-Native Reader).Natural Sciences and Engineering Research Council (NSERC) of Canada and the University of Lethbridg

Multi-document summarization based on document clustering and neural sentence fusion

In this thesis, we have approached a technique for tackling abstractive text summarization tasks with state-of-the-art results. We have proposed a novel method to improve multidocument summarization. The lack of large multi-document human-authored summaries needed to train seq2seq encoder-decoder models and the inaccuracy in representing multiple long documents into a fixed size vector inspired us to design complementary models for two different tasks such as sentence clustering and neural sentence fusion. In this thesis, we minimize the risk of producing incorrect fact by encoding a related set of sentences as an input to the encoder. We applied our complementary models to implement a full abstractive multi-document summarization system which simultaneously considers importance, coverage, and diversity under a desired length limit. We conduct extensive experiments for all the proposed models which bring significant improvements over the state-of-the-art methods across different evaluation metrics.Natural Sciences and Engineering Research Council (NSERC) of Canada and the University of Lethbridg