Diversity driven Attention Model for Query-based Abstractive Summarization (1704.08300v2)

Abstract: Abstractive summarization aims to generate a shorter version of the document covering all the salient points in a compact and coherent fashion. On the other hand, query-based summarization highlights those points that are relevant in the context of a given query. The encode-attend-decode paradigm has achieved notable success in machine translation, extractive summarization, dialog systems, etc. But it suffers from the drawback of generation of repeated phrases. In this work we propose a model for the query-based summarization task based on the encode-attend-decode paradigm with two key additions (i) a query attention model (in addition to document attention model) which learns to focus on different portions of the query at different time steps (instead of using a static representation for the query) and (ii) a new diversity based attention model which aims to alleviate the problem of repeating phrases in the summary. In order to enable the testing of this model we introduce a new query-based summarization dataset building on debatepedia. Our experiments show that with these two additions the proposed model clearly outperforms vanilla encode-attend-decode models with a gain of 28% (absolute) in ROUGE-L scores.

• The paper proposes a novel diversity-driven attention model and a dynamic query attention mechanism to improve query-based abstractive summarization by preventing phrase repetition.
• Authors created a new dataset using Debatepedia for query-based summarization and achieved a 28% ROUGE-L improvement over baselines with their model.
• The diversity-driven attention approach, particularly its LSTM formulation, offers potential for improving other natural language generation tasks beyond summarization.

Diversity Driven Attention Model for Query-based Abstractive Summarization: An Overview

The paper entitled "Diversity driven Attention Model for Query-based Abstractive Summarization" addresses the critical yet underexplored problem of query-based abstractive text summarization. This work situates itself in the broader context of neural models that utilize the encode-attend-decode paradigm, a framework that has proven effective for several natural language generation tasks. The key contributions of this paper lie in the innovations it introduces to tackle the commonly observed problem of repeated phrases generated by existing models.

Background and Motivation

Abstractive summarization condenses a given document to cover salient points coherently, unlike extractive summarization, which merely selects sentences from the text. The task becomes more complex when it is query-based, as the summary should focus on aspects of the document pertinent to a specific query. Traditional models based on encode-attend-decode struggle with this when tasked with generating text, often redundantly repeating phrases.

Key Innovations

The authors propose a novel model to enhance query-based summarization by modifying the encode-attend-decode approach through two primary enhancements:

1. Query Attention Model: This model allows the system to adjust focus dynamically across different parts of a query at each time step, as opposed to treating the query as a static entity. This dynamic attention mechanism aids in more accurately pinpointing relevant information within the document context of the query.
2. Diversity Based Attention Model: To counteract the issue of repeated phrases, this model ensures that context vectors (encapsulating what the model knows at a given point) fed into the decoder are diverse from one another across time steps. The paper proposes several formulations, including a straight orthogonalization of successive vectors and a softer, gated approach using LSTM cells. The LSTM-based approach stands out for maintaining a balance between diversity and attention retention, allowing the model to revisit relevant document sections when needed.

Experimental Contributions

A significant practical hurdle in the field has been the lack of datasets tailored for query-based abstractive summarization. To this end, the authors construct a dataset derived from Debatepedia, encompassing various topics and query-document-summary triples. This dataset serves as a testbed to evaluate model performances and benchmarks improvements brought forth by their innovations.

Their experiments, quantified through ROUGE evaluation metrics, reveal notable improvements. Specifically, their model achieves a 28% absolute gain in ROUGE-L scores over baseline models, a substantial leap that underscores the efficacy of their diversity-centric approach. Additionally, when compared to state-of-the-art models dealing with repetition problems, the proposed model demonstrates a 7% absolute improvement in performance.

Implications for Future Developments

The paper's contributions lie not only in solving immediate challenges of phrase repetition but also in crafting a general architecture potentially adaptable for other forms of natural language generation, including dialogue systems and broader summarization tasks beyond query-based confines. The flexibility afforded by the diversity model, especially in its LSTM-based variants, presents intriguing possibilities for applications requiring contextual and dynamic interpretation over extended sequences.

In conclusion, this work delivers an important step forward in query-based abstractive summarization, leveraging the principles of diversity within attention mechanisms. It sets a foundation for further exploration, leveraging neural models to achieve more coherent, focused, and diverse text outputs. This research has implications for developing more sophisticated natural language generation systems, potentially transforming how information is succinctly distilled and presented in user-intended contexts.