Generating Abstractive Summaries from Meeting Transcripts

Summaries of meetings are very important as they convey the essential content of discussions in a concise form. 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.Comment: 10 pages, Proceedings of the 2015 ACM Symposium on Document Engineering, DocEng' 201

Toward Extractive Summarization of Online Forum Discussions via Hierarchical Attention Networks

Forum threads are lengthy and rich in content. Concise thread summaries will benefit both newcomers seeking information and those who participate in the discussion. Few studies, however, have examined the task of forum thread summarization. In this work we make the first attempt to adapt the hierarchical attention networks for thread summarization. The model draws on the recent development of neural attention mechanisms to build sentence and thread representations and use them for summarization. Our results indicate that the proposed approach can outperform a range of competitive baselines. Further, a redundancy removal step is crucial for achieving outstanding results.Comment: 5 page

Toward Extractive Summarization of Online Forum Discussions via Hierarchical Attention Networks

Forum threads are lengthy and rich in content. Concise thread summaries will benefit both newcomers seeking information and those who participate in the discussion. Few studies, however, have examined the task of forum thread summarization. In this work we make the first attempt to adapt the hierarchical attention networks for thread summarization. The model draws on the recent development of neural attention mechanisms to build sentence and thread representations and use them for summarization. Our results indicate that the proposed approach can outperform a range of competitive baselines. Further, a redundancy removal step is crucial for achieving outstanding results.Comment: 5 page

Unsupervised Summarization for Chat Logs with Topic-Oriented Ranking and Context-Aware Auto-Encoders

Automatic chat summarization can help people quickly grasp important information from numerous chat messages. Unlike conventional documents, chat logs usually have fragmented and evolving topics. In addition, these logs contain a quantity of elliptical and interrogative sentences, which make the chat summarization highly context dependent. In this work, we propose a novel unsupervised framework called RankAE to perform chat summarization without employing manually labeled data. RankAE consists of a topic-oriented ranking strategy that selects topic utterances according to centrality and diversity simultaneously, as well as a denoising auto-encoder that is carefully designed to generate succinct but context-informative summaries based on the selected utterances. To evaluate the proposed method, we collect a large-scale dataset of chat logs from a customer service environment and build an annotated set only for model evaluation. Experimental results show that RankAE significantly outperforms other unsupervised methods and is able to generate high-quality summaries in terms of relevance and topic coverage.Comment: Accepted by AAAI 2021, 9 page

WhatsApp intel·ligent (Anàlisi d'una conversa de WhatsApp)

WhatsApp ha esdevingut una de les principals eines de comunicació informal en els darrers anys. Aquest fet ha comportat un canvi en la qualitat de les nostres comunicacions: ara podem intercanviar informació des del nostre dispositiu mòbil instantàniament i tenim la possibilitat d'accedir-hi en qualsevol moment. Encara que totes aquestes característiques ens faciliten molts aspectes de la vida, també ens obliguen a estar disponibles les 24 hores del dia pendents dels fils de les nostres converses. Aquest projecte s'ha desenvolupat per tal d'ajudar a analitzar i resumir converses en grup en les quals s'hagi produït un gran nombre de missatges on nosaltres no hàgim estat presents a través d'una eina senzilla i fàcil d'utilitzar. A causa de la gran quantitat d'informació que es pot intercanviar en aquesta plataforma i dels diferents tipus (text, icones, imatges, vídeo, veu), aquest projecte està enfocat en converses de grup les quals tinguin objectius molt concrets, com ara decidir llocs, regals, viatges, festes…WhatsApp has become one of the main communication tools in the last years. This fact has led us to a change in the quality of our communications, now we can exchange information instantly from our mobile devices and we have the ability to access to them at any time. Although all these features can facilitate many aspects of our lives, it makes us to be available 24 hours a day, attentive to the threads of our conversations. This project has been developed to help to analyse and summarize group conversations in which there has been a big number of messages and where we were not present through a simple and easy to use tool. Due to the large amount of information that can be exchanged for this platform and the different types (text, icons, images, video, voice), this project is focused on group conversations that have very specific goals, such as deciding sites, gifts, trips, parties…WhatsApp se ha convertido en una de las principales herramientas de comunicación informal en los últimos años. Este hecho ha supuesto un cambio en la calidad de nuestras comunicaciones: ahora podemos intercambiar información desde nuestro dispositivo móvil instantáneamente y tenemos la posibilidad de acceder a ella en cualquier momento. Aunque todas estas características nos facilitan muchos aspectos de la vida, también nos obligan a estar disponibles las 24 horas del día pendientes de nuestras conversaciones. Este proyecto se ha desarrollado para ayudar a analizar y resumir conversaciones en grupo en las que se haya producido un gran número de mensajes donde nosotros no hayamos estado presentes a través de una herramienta sencilla y fácil de usar. Debido a la gran cantidad de información que se puede intercambiar en esta plataforma y de los diferentes tipos (texto, iconos, imágenes, vídeo, voz), este proyecto está enfocado en conversaciones de grupo las que tengan objetivos muy concretos, tales como decidir lugares, regalos, viajes, fiestas