This paper addresses the problem of summarizing decisions in spoken meetings: our goal is to produce a concise decision abstract for each meeting decision. We explore and compare token-level and dialogue act-level automatic summarization methods using both unsupervised and supervised learning frameworks. In the supervised summarization setting, and given true clusterings of decision-related utterances, we find that token-level summaries that employ discourse context can approach an upper bound for decision abstracts derived directly from dialogue acts. In the unsupervised summarization setting,we find that summaries based on unsupervised partitioning of decision-related utterances perform comparably to those based on partitions generated using supervised techniques (0.22 ROUGE-F1 using LDA-based topic models vs. 0.23 using SVMs).
There exists much previous research on automatic text summarization using corpus-based, knowledge-based or statistical methods @cite_11 @cite_9 . Dialogue summarization methods, however, generally try to account for the special characteristics of speech. Among early work in this subarea, investigates speech summarization based on maximal marginal relevance (MMR) and cross-speaker linking of information. Popular supervised methods for summarizing speech --- including maximum entropy, conditional random fields (CRFs), and support vector machines (SVMs) --- are investigated in , and . Techniques for determining semantic similarity are used for selecting relevant utterances in . Studies in show that decisions are considered to be one of the most important outputs of meetings. And in recent years, there has been much research on detecting decision-related DAs. , for example, propose maximum entropy classification techniques to identify DRDAs in meetings; develop a model of decision-making dialogue structure and detect decision DAs based on it; and implement a real-time decision detection system.
@cite_9 @cite_11
2169218343 2017187090 2122514299 2104483432
In this paper we present an opinion summarization technique in spoken dialogue systems. Opinion mining has been well studied for years, but very few have considered its application in spoken dialogue systems. Review summarization, when applied to real dialogue systems, is much more complicated than pure text-based summarization. We conduct a systematic study on dialogue-system-oriented review analysis and propose a three-level framework for a recommendation dialogue system. In previous work we have explored a linguistic parsing approach to phrase extraction from reviews. In this paper we will describe an approach using statistical models such as decision trees and SVMs to select the most representative phrases from the extracted phrase set. We will also explain how to generate informative yet concise review summaries for dialogue purposes. Experimental results in the restaurant domain show that the proposed approach using decision tree algorithms achieves an outperformance of 13 compared to SVM models and an improvement of 36 over a heuristic rule baseline. Experiments also show that the decision-tree-based phrase selection model can achieve rather reliable predictions on the phrase label, comparable to human judgment. The proposed statistical approach is based on domain-independent learning features and can be extended to other domains effectively. We describe a probabilistic approach to content selection for meeting summarization. We use skipchain Conditional Random Fields (CRF) to model non-local pragmatic dependencies between paired utterances such as Question-Answer that typically appear together in summaries, and show that these models outperform linear-chain CRFs and Bayesian models in the task. We also discuss different approaches for ranking all utterances in a sequence using CRFs. Our best performing system achieves 91.3 of human performance when evaluated with the Pyramid evaluation metric, which represents a 3.9 absolute increase compared to our most competitive non-sequential classifier. Most previous work on trainable language generation has focused on two paradigms: (a) using a generation decisions of an existing generator. Both approaches rely on the existence of a handcrafted generation component, which is likely to limit their scalability to new domains. The first contribution of this article is to present Bagel, a fully data-driven generation method that treats the language generation task as a search for the most likely sequence of semantic concepts and realization phrases, according to Factored Language Models (FLMs). As domain utterances are not readily available for most natural language generation tasks, a large creative effort is required to produce the data necessary to represent human linguistic variation for nontrivial domains. This article is based on the assumption that learning to produce paraphrases can be facilitated by collecting data from a large sample of untrained annotators using crowdsourcing—rather than a few domain experts—by relying on a coarse meaning representation. A second contribution of this article is to use crowdsourced data to show how dialogue naturalness can be improved by learning to vary the output utterances generated for a given semantic input. Two data-driven methods for generating paraphrases in dialogue are presented: (a) by sampling from the n-best list of realizations produced by Bagel's FLM reranker; and (b) by learning a structured perceptron predicting whether candidate realizations are valid paraphrases. We train Bagel on a set of 1,956 utterances produced by 137 annotators, which covers 10 types of dialogue acts and 128 semantic concepts in a tourist information system for Cambridge. An automated evaluation shows that Bagel outperforms utterance class LM baselines on this domain. A human evaluation of 600 resynthesized dialogue extracts shows that Bagel's FLM output produces utterances comparable to a handcrafted baseline, whereas the perceptron classifier performs worse. Interestingly, human judges find the system sampling from the n-best list to be more natural than a system always returning the first-best utterance. The judges are also more willing to interact with the n-best system in the future. These results suggest that capturing the large variation found in human language using data-driven methods is beneficial for dialogue interaction. Statistical approaches to automatic text summarization based on term frequency continue to perform on par with more complex summarization methods. To compute useful frequency statistics, however, the semantically important words must be separated from the low-content function words. The standard approach of using an a priori stopword list tends to result in both undercoverage, where syntactical words are seen as semantically relevant, and overcoverage, where words related to content are ignored. We present a generative probabilistic modeling approach to building content distributions for use with statistical multi-document summarization where the syntax words are learned directly from the data with a Hidden Markov Model and are thereby deemphasized in the term frequency statistics. This approach is compared to both a stopword-list and POS-tagging approach and our method demonstrates improved coverage on the DUC 2006 and TAC 2010 datasets using the ROUGE metric.