A real-time system for summarizing human-human spontaneous spoken dialogues

Kameyama, Megumi; Kawai, Goh; Arima, I.

doi:10.1109/icslp.1996.607453

Cited by 9 publications

(5 citation statements)

References 3 publications

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“…The resulting model may be transformed using heuristic rules (e.g. [Hahn and Reimer 1999;Paice and Jones 1993]) to yield a representation of the final summary contents, but some approaches perform this step implicitly during parsing (e.g., [Kameyama et al 1996]). Using natural language generation components, the final text can be generated from the representation thus derived.…”

Section: Approaches To Speech Summarizationmentioning

confidence: 99%

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Extrinsic Summarization Evaluation: A Decision Audit Task

Murray

Kleinbauer

Poller

et al.

Machine Learning for Multimodal Interaction

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In this work we describe a large-scale extrinsic evaluation of automatic speech summarization technologies for meeting speech. The particular task is a decision audit, wherein a user must satisfy a complex information need, navigating several meetings in order to gain an understanding of how and why a given decision was made. We compare the usefulness of extractive and abstractive technologies in satisfying this information need, and assess the impact of automatic speech recognition (ASR) errors on user performance. We employ several evaluation methods for participant performance, including post-questionnaire data, human subjective and objective judgments, and a detailed analysis of participant browsing behavior. We find that while ASR errors affect user satisfaction on an information retrieval task, users can adapt their browsing behavior to complete the task satisfactorily. Results also indicate that users consider extractive summaries to be intuitive and useful tools for browsing multi-modal meeting data. We discuss areas in which automatic summarization techniques can be improved in comparison with gold-standard meeting abstracts.

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Section: Approaches To Speech Summarizationmentioning

confidence: 99%

“…[Saggion and Lapalme 2002]). While Endres-Niggemeyer ([1998]; chapter 5) has reviewed the abstractive summarization of textual documents, less research has been done on the abstractive summarization of spoken discourse although some promising approaches exist, e.g., [Kameyama et al 1996;Alexandersson 2003].…”

Section: Approaches To Speech Summarizationmentioning

confidence: 99%

Extrinsic Summarization Evaluation: A Decision Audit Task

Murray

Kleinbauer

Poller

et al.

Machine Learning for Multimodal Interaction

View full text Add to dashboard Cite

show abstract

“…However, with the increase of information in the form of dialogues both written and spoken, the need for dialogue summarization has increased as well. Some of the earlier work that focuses on summarizing task-oriented dialogues involves generating summaries of conversations about conference room reservations (Kameyama et al, 1996), scheduling, travel planning, and hotel reservations (Reithinger et al, 2000).…”

Section: Modeling Smart Attributesmentioning

confidence: 99%

Modeling Health Coaching Dialogues for Behavioral Goal Extraction

Gupta

Eugenio

Ziebart

et al. 2019

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Health coaching has been identified as a successful method for motivating and maintaining health behavior changes. Unfortunately, personal health coaching is time-and resourceintensive, and cannot scale up. Previous research focuses on developing conversational systems that can provide automated coaching to patients. But most of these systems rely on a predefined set of input/output mappings, focus more on general goal setting, and do not provide follow-up during goal implementation. Therefore, my research contributes to building a virtual assistant health coach to help patients set and achieve a Specific, Measurable, Attainable, Realistic, and Time-bound (SMART) goal.The first step towards building a successful domain-specific dialogue system is to understand the users' needs and the environment in which it will be used. Since no other public health coaching dialogue data exists, we collected data that I annotated for a variety of features of interest. These annotations include domain-specific slots called SMART attributes at the word level, the intent behind a participant's text message called dialogue acts, and higher-level portions of the dialogue performing a specific function called stages and phases. I then used these annotations to build the language understanding component of the dialogue system and design the goal extraction pipeline, which extracts the physical activity goal the user and coach negotiate via text messages. I proposed a two-step goal extraction process that uses phases and dialogue acts to identify the correct SMART goal attributes and show that dialogue acts help more than the higher-level conversation flow captured by phases. These goal summaries will xv SUMMARY (Continued) help the health coaches to easily recall patients' past goals and use them to suggest a realistic future goal. Finally, I assessed the usability of the generated goal summaries with the help of three health coaches in an offline setting.

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“…We had during the year developed a Japanese version of FASTUS for use in a conference room reservation task for a commercial client. This system read and extracted the relevant information from romanji input, and was later developed into a real-time spontaneous dialogue summarizer (Kameyama et al, 1995). For MUC-5 we converted this to handle kanji characters as well, and used the Grasper-based interface to define rules for recognizing joint ventures in both English and Japanese business news.…”

Section: History Of the Fastus Systemmentioning

confidence: 99%

FASTUS: A System for Extracting Information from Natural-Language Text

Hobbs¹,

Appelt²,

Bear³

et al. 1992

101

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FASTUS is a system for extracting information from free text in English, and potentially other languages as well, for entry into a database, and potentially for other applications. It works essentially as a cascaded, nondeterministic finite state automaton. There are four steps in the operation of FASTUS. In Step 1 sentences are scanned for certain trigger words to determine whether further processing should be done. In Step 2 noun groups, verb groups, and prepositions and some other particles are recognized. The input to Step 3 is the sequence of phrases recognized in Step 2; patterns of interest are identified in Step 3 and corresponding "incident structures" are built up. In Step 4 incident structures that derive from the same incident are identified and merged, and these are used in generating database entries. FASTUS is an order of magnitude faster than any comparable system; it can process a news report in an average of less than eleven seconds. This translates directly into fast development time. In the three and a half weeks between its first use and the MUC-4 evaluation in May 1992, we were able to build up its domain knowledge to a point where it was among the leaders in the evaluation.

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A real-time system for summarizing human-human spontaneous spoken dialogues

Cited by 9 publications

References 3 publications

Extrinsic Summarization Evaluation: A Decision Audit Task

Extrinsic Summarization Evaluation: A Decision Audit Task

Modeling Health Coaching Dialogues for Behavioral Goal Extraction

FASTUS: A System for Extracting Information from Natural-Language Text

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