Abstractive Summarization of Spoken and Written Conversations Based on Phrasal Queries

By Yashar Mehdad, Giuseppe Carenini, Raymond T. Ng. 2014

58 citations recorded by the source corpus.

We propose a novel abstractive querybased summarization system for conversations, where queries are defined as phrases reflecting a user information needs. We rank and extract the utterances in a conversation based on the overall content and the phrasal query information. We cluster the selected sentences based on their lexical similarity and aggregate the sentences in each cluster by means of a word graph model. We propose a ranking strategy to select the best path in the constructed graph as a query-based abstract sentence for each cluster. A resulting summary consists of abstractive sentences representing the phrasal query information and the overall content of the conversation. Automatic and manual evaluation results over meeting, chat and email conversations show that our approach significantly outperforms baselines and previous extractive models.

Subfields: Artificial Intelligence, Summarization, Speech & Audio

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jiajun Zhang
2. Chengqing Zong
3. Iwona Mochol
4. Aleksander Wawer
5. Bogdan Gliwa
6. Maciej Biesek
7. Yu Zhou
8. Prakhar Ganesh

In Summarization

1. Iwona Mochol
2. Aleksander Wawer
3. Bogdan Gliwa
4. Maciej Biesek
5. Yu Zhou
6. Chengqing Zong
7. Jiajun Zhang
8. Prakhar Ganesh

In Speech & Audio

1. Prakhar Ganesh
2. Saket Dingliwal

Citations in the corpus

• SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization (2019, methodology): “The challenges posed by the abstractive dialogue summarization task have been discussed in the literature with regard to AMI meeting corpus (McCowan et al., 2005), e.g. Banerjee et al. (2015), Mehdad et al. (2014), Goo and Chen (2018).”
• Abstractive Cross-Language Summarization via Translation Model Enhanced Predicate Argument Structure Fusing (2016, background): “In monolingual multi-document summarization, abstraction-based methods [7], [27]–[35] are deeply explored in recent years.”
• Abstractive Summarization of Spoken and Written Conversation (2019, methodology): “Query based ranking approach was used in (Mehdad et al. 2014).”