Citation Handling: Processing Citation Texts in Scientific Documents

Citation sentences (sentences that cite other papers) play a key role in the summarization of scientific articles. However, a citation-based summarization system that depends on generic natural language processing components, such as parsers or sentence compressors, will perform poorly if those components cannot handle citations correctly. In this thesis, I examine the effect of citation handling on parsing, sentence compression, and multi-document summarization. There are two types of citations that occur in citation sentences: constituent citations and parenthetical citations. I propose an automatic citation classifier based on training data created through Mechanical Turk tasks. I demonstrate that the use of type-specific citation handling as pre-processing improves the performance of a state-of-the-art generic parser, both for quality of the parse trees and running time. Extrinsic evaluations demonstrate that improving the performance of a parser on citation sentences in turn improves the performance of a sentence compressor, Trimmer (Zajic et al., 2007), and a multi-document summarization system, MASCS, according to several summarization measures

LCSTS: A Large Scale Chinese Short Text Summarization Dataset

Automatic text summarization is widely regarded as the highly difficult problem, partially because of the lack of large text summarization data set. Due to the great challenge of constructing the large scale summaries for full text, in this paper, we introduce a large corpus of Chinese short text summarization dataset constructed from the Chinese microblogging website Sina Weibo, which is released to the public {http://icrc.hitsz.edu.cn/Article/show/139.html}. This corpus consists of over 2 million real Chinese short texts with short summaries given by the author of each text. We also manually tagged the relevance of 10,666 short summaries with their corresponding short texts. Based on the corpus, we introduce recurrent neural network for the summary generation and achieve promising results, which not only shows the usefulness of the proposed corpus for short text summarization research, but also provides a baseline for further research on this topic.Comment: Recently, we received feedbacks from Yuya Taguchi from NAIST in Japan and Qian Chen from USTC of China, that the results in the EMNLP2015 version seem to be underrated. So we carefully checked our results and find out that we made a mistake while using the standard ROUGE. Then we re-evaluate all methods in the paper and get corrected results listed in Table 2 of this versio

Deep Recurrent Generative Decoder for Abstractive Text Summarization

We propose a new framework for abstractive text summarization based on a sequence-to-sequence oriented encoder-decoder model equipped with a deep recurrent generative decoder (DRGN). Latent structure information implied in the target summaries is learned based on a recurrent latent random model for improving the summarization quality. Neural variational inference is employed to address the intractable posterior inference for the recurrent latent variables. Abstractive summaries are generated based on both the generative latent variables and the discriminative deterministic states. Extensive experiments on some benchmark datasets in different languages show that DRGN achieves improvements over the state-of-the-art methods.Comment: 10 pages, EMNLP 201

Chinese Spoken Document Summarization Using Probabilistic Latent Topical Information

[[abstract]]The purpose of extractive summarization is to automatically select a number of indicative sentences, passages, or paragraphs from the original document according to a target summarization ratio and then sequence them to form a concise summary. In the paper, we proposed the use of probabilistic latent topical information for extractive summarization of spoken documents. Various kinds of modeling structures and learning approaches were extensively investigated. In addition, the summarization capabilities were verified by comparison with the conventional vector space model and latent semantic indexing model, as well as the HMM model. The experiments were performed on the Chinese broadcast news collected in Taiwan. Noticeable performance gains were obtained.

A Multilingual Study of Compressive Cross-Language Text Summarization

Cross-Language Text Summarization (CLTS) generates summaries in a language different from the language of the source documents. Recent methods use information from both languages to generate summaries with the most informative sentences. However, these methods have performance that can vary according to languages, which can reduce the quality of summaries. In this paper, we propose a compressive framework to generate cross-language summaries. In order to analyze performance and especially stability, we tested our system and extractive baselines on a dataset available in four languages (English, French, Portuguese, and Spanish) to generate English and French summaries. An automatic evaluation showed that our method outperformed extractive state-of-art CLTS methods with better and more stable ROUGE scores for all languages

A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data

It is a challenging and practical research problem to obtain effective compression of lengthy product titles for E-commerce. This is particularly important as more and more users browse mobile E-commerce apps and more merchants make the original product titles redundant and lengthy for Search Engine Optimization. Traditional text summarization approaches often require a large amount of preprocessing costs and do not capture the important issue of conversion rate in E-commerce. This paper proposes a novel multi-task learning approach for improving product title compression with user search log data. In particular, a pointer network-based sequence-to-sequence approach is utilized for title compression with an attentive mechanism as an extractive method and an attentive encoder-decoder approach is utilized for generating user search queries. The encoding parameters (i.e., semantic embedding of original titles) are shared among the two tasks and the attention distributions are jointly optimized. An extensive set of experiments with both human annotated data and online deployment demonstrate the advantage of the proposed research for both compression qualities and online business values.Comment: 8 Pages, accepted at AAAI 201

Order-Preserving Abstractive Summarization for Spoken Content Based on Connectionist Temporal Classification

Connectionist temporal classification (CTC) is a powerful approach for sequence-to-sequence learning, and has been popularly used in speech recognition. The central ideas of CTC include adding a label "blank" during training. With this mechanism, CTC eliminates the need of segment alignment, and hence has been applied to various sequence-to-sequence learning problems. In this work, we applied CTC to abstractive summarization for spoken content. The "blank" in this case implies the corresponding input data are less important or noisy; thus it can be ignored. This approach was shown to outperform the existing methods in term of ROUGE scores over Chinese Gigaword and MATBN corpora. This approach also has the nice property that the ordering of words or characters in the input documents can be better preserved in the generated summaries.Comment: Accepted by Interspeech 201