Summaries of meetings are very important as they convey the essential content of discussions in a concise form. Both participants and non-participants are interested in the summaries of meetings to plan for their future work. Generally, it is time consuming to read and understand the whole documents. Therefore, summaries play an important role as the readers are interested in only the important context of discussions. In this work, we address the task of meeting document summarization. Automatic summarization systems on meeting conversations developed so far have been primarily extractive, resulting in unacceptable summaries that are hard to read. The extracted utterances contain disfluencies that affect the quality of the extractive summaries. To make summaries much more readable, we propose an approach to generating abstractive summaries by fusing important content from several utterances. We first separate meeting transcripts into various topic segments, and then identify the important utterances in each segment using a supervised learning approach. The important utterances are then combined together to generate a one-sentence summary. In the text generation step, the dependency parses of the utterances in each segment are combined together to create a directed graph. The most informative and well-formed sub-graph obtained by integer linear programming (ILP) is selected to generate a one-sentence summary for each topic segment. The ILP formulation reduces disfluencies by leveraging grammatical relations that are more prominent in non-conversational style of text, and therefore generates summaries that is comparable to human-written abstractive summaries. Experimental results show that our method can generate more informative summaries than the baselines. In addition, readability assessments by human judges as well as log-likelihood estimates obtained from the dependency parser show that our generated summaries are significantly readable and well-formed.
Liu and Liu used sentence compression to generate summaries of meetings. However, they reported that the quality of the generated summaries are not so good and there is a potential limit to apply such methods to summarization. Murray mapped conversations to an ontology that was complemented with a Natural language generation (NLG) component used for transforming utterances to summaries. The corresponding full summarization system was later presented in @cite_1 , where a user study was conducted on the abstractive summaries that were generated. However, the full summarization system involved extensive manual labor to set specific speakers, entities, etc. in a template before using an NLG realizer to generate the summaries. Lu and Cardie proposed a method that learns templates from the human written summaries and generates the summaries of decisions and actions of meetings by using the best set of templates for a particular summary ranked using a greedy approach. In contrast, we cannot use templates because we assume that the type of a conversation (action, decision, etc) is not known apriori.
@cite_1
1923455183 2122514299 2169218343 1061069728
In this paper we present a complete system for automatically generating natural language abstracts of meeting conversations. This system is comprised of components relating to interpretation of the meeting documents according to a meeting ontology, transformation or content selection from that source representation to a summary representation, and generation of new summary text. In a formative user study, we compare this approach to gold-standard human abstracts and extracts to gauge the usefulness of the different summary types for browsing meeting conversations. We find that our automatically generated summaries are ranked significantly higher than human-selected extracts on coherence and usability criteria. More generally, users demonstrate a strong preference for abstract-style summaries over extracts. 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. 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. Automatic summarization techniques on meeting conversations developed so far have been primarily extractive, resulting in poor summaries. To improve this, we propose an approach to generate abstractive summaries by fusing important content from several utterances. Any meeting is generally comprised of several discussion topic segments. For each topic segment within a meeting conversation, we aim to generate a one sentence summary from the most important utterances using an integer linear programming-based sentence fusion approach. Experimental results show that our method can generate more informative summaries than the baselines.