This paper proposes a novel extractive summarization method for speech dialogues between agents and customers in contact centers. The proposed method does not require any extra cost for applying the method such as preparing rules or creating training data. Conventional methods such as the tf*idf method, which gives importance to characteristic words in an input text, can miss the essential poin...

The macroscopical characters of the leaves, leaf constants, physico-chemical constants, extractive values, colour, consistency, pH, extractive values with different solvents, micro chemical test, fluorescence characters of liquid extracts and leaf powder after treatment with different chemical reagents under visible and UV light at 254mn, measurement of cell and tissues were studied to fix some...

Extractive summarization is the strategy of concatenating extracts taken from a corpus into a summary, while abstractive summarization involves paraphrasing the corpus using novel sentences. We define a novel measure of corpus controversiality of opinions contained in evaluative text, and report the results of a user study comparing extractive and NLG-based abstractive summarization at differen...

Sentence compression has attracted much interest in recent years, but most sentence compressors are extractive, i.e., they only delete words. There is a lack of appropriate datasets to train and evaluate abstractive sentence compressors, i.e., methods that apart from deleting words can also rephrase expressions. We present a new dataset that contains candidate extractive and abstractive compres...

In this paper we address two key challenges for extractive multi-document summarization: the search problem of finding the best scoring summary and the training problem of learning the best model parameters. We propose an A* search algorithm to find the best extractive summary up to a given length, which is both optimal and efficient to run. Further, we propose a discriminative training algorit...

Supervised methods for extractive speech summarization require a large training set. Summary annotation is often expensive and time consuming. In this paper, we exploit semi-supervised approaches to leverage unlabeled data. In particular, we investigate co-training for the task of extractive meeting summarization. Compared with text summarization, speech summarization task has its unique charac...

Since many existing systems have been still working even these system have several problems in extending or maintaining. This is big obstacle for company to adopt the product line engineering. Extractive approach can be alternative approach when the company has their own system and adopt product line engineering. In extractive approach, commonality and variability modeling is critical also. In ...

In this paper we tackle the problem of automatic caption generation for news images. Our approach leverages the vast resource of pictures available on the web and the fact that many of them are captioned. Inspired by recent work in summarization, we propose extractive and abstractive caption generation models. They both operate over the output of a probabilistic image annotation model that prep...

Single document summarization is the task of producing a shorter version of a document while preserving its principal information content. In this paper we conceptualize extractive summarization as a sentence ranking task and propose a novel training algorithm which globally optimizes the ROUGE evaluation metric through a reinforcement learning objective. We use our algorithm to train a neural ...