Supervised and Unsupervised Learning for Sentence Compression
نویسندگان
چکیده
In Statistics-Based Summarization Step One: Sentence Compression, Knight and Marcu (Knight and Marcu, 2000) (K&M) present a noisy-channel model for sentence compression. The main difficulty in using this method is the lack of data; Knight and Marcu use a corpus of 1035 training sentences. More data is not easily available, so in addition to improving the original K&M noisy-channel model, we create unsupervised and semi-supervised models of the task. Finally, we point out problems with modeling the task in this way. They suggest areas for future research.
منابع مشابه
Overcoming the Lack of Parallel Data in Sentence Compression
A major challenge in supervised sentence compression is making use of rich feature representations because of very scarce parallel data. We address this problem and present a method to automatically build a compression corpus with hundreds of thousands of instances on which deletion-based algorithms can be trained. In our corpus, the syntactic trees of the compressions are subtrees of their unc...
متن کاملمقایسه روش‌های مختلف یادگیری ماشین در خلاصه‌سازی استخراجی گفتار به گفتار فارسی بدون استفاده از رونوشت
In this paper, extractive speech summarization using different machine learning algorithms was investigated. The task of Speech summarization deals with extracting important and salient segments from speech in order to access, search, extract and browse speech files easier and in a less costly manner. In this paper, a new method for speech summarization without using automatic speech recognitio...
متن کاملUnsupervised SMT for a low-resourced language pair
This paper presents an unsupervised method in application of extracting parallel sentence pairs from a comparable corpus. A translation system is used to mine the comparable corpus and to withdraw the parallel sentence pairs. An iteration process is implemented not only to increase the number of extracted parallel sentence pairs but also to improve the quality of translation system. A compariso...
متن کاملSentence Compression For Automatic Subtitling
This paper investigates sentence compression for automatic subtitle generation using supervised machine learning. We present a method for sentence compression as well as discuss generation of training data from compressed Finnish sentences, and different approaches to the problem. The method we present outperforms state-of-the-art baseline in both automatic and human evaluation. On real data, 4...
متن کاملUnsupervised model compression for multilayer bootstrap networks
Recently, multilayer bootstrap network (MBN) has demonstrated promising performance in unsupervised dimensionality reduction. It can learn compact representations in standard data sets, i.e. MNIST and RCV1. However, as a bootstrap method, the prediction complexity of MBN is high. In this paper, we propose an unsupervised model compression framework for this general problem of unsupervised boots...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2005