Controlling Output Length in Neural Encoder-Decoders (1609.09552v1)

Abstract: Neural encoder-decoder models have shown great success in many sequence generation tasks. However, previous work has not investigated situations in which we would like to control the length of encoder-decoder outputs. This capability is crucial for applications such as text summarization, in which we have to generate concise summaries with a desired length. In this paper, we propose methods for controlling the output sequence length for neural encoder-decoder models: two decoding-based methods and two learning-based methods. Results show that our learning-based methods have the capability to control length without degrading summary quality in a summarization task.

Controlling Output Length in Neural Encoder-Decoders: An Expert Perspective

This paper discusses the integration of length-control mechanisms into neural encoder-decoder models, particularly within the context of text summarization. The research expands on the conventional application of encoder-decoder architectures, which have demonstrated proficiency across various sequence generation tasks, including but not limited to image captioning, parsing, and dialogue response generation. The focus here is on sentence summarization, a domain wherein concise output is often imperative according to user or application requirements.

Methodological Contributions

The authors introduce four distinct methods to regulate the output length of encoder-decoder models: two decoding-based methods labeled as $fixLen$ and $fixRng$, and two learning-based methods designated as $LenEmb$ and $LenInit$. These methods aim to offer solutions across diverse scenarios where sequence output length specifications vary.

1. $fixLen$ is a simplistic method whereby the model is inhibited from generating an end-of-sentence token until a pre-specified length is reached. This technique ensures the generation of sequences with a guaranteed length but may lack flexibility.
2. $fixRng$ introduces a range-based constraint during beam search, allowing summary generation within a specified length interval. This supports some adaptability by retaining sequences only if they fall within the defined limits.
3. $LenEmb$ utilizes length embeddings as input to the LSTM in the decoder, providing information on remaining length at each time step. This supports the decoder in planning the summary length dynamically.
4. $LenInit$ incorporates the desired length into the initial state of the memory cell within the LSTM decoder, inducing implicit length management throughout the decoding process.

The methodological innovations come with a clear focus on harnessing the network's capacity to adaptively manage its output without compromising on summary quality, as shown by their performance on benchmark datasets.

Empirical Evaluation and Results

Experiments were conducted with the DUC2004 task-1 dataset, evaluating the proposed methods across a range of predefined length constraints (30, 50, and 75 bytes). The results, as measured by ROUGE scores, indicate that learning-based approaches, particularly $LenEmb$, generally outperform decoding-based methods in scenarios where longer summaries are needed. Notably, $LenEmb$ achieved higher ROUGE scores in both the 50-byte and 75-byte tests, suggesting that the model effectively integrates the added length context.

It is important to note the practical implications of these findings: learning-based models can adjust the length while maintaining competitive performance, affording greater flexibility and utility across applications demanding varied output lengths.

Theoretical and Practical Implications

The introduction of length-controlled encoder-decoder models has both theoretical and practical dimensions. Theoretically, the paper broadens our understanding of sequence-to-sequence modeling, demonstrating that neural networks can be modified internally to handle length constraints dynamically. This significantly enhances the encoder-decoder paradigm in NLP tasks, offering a more nuanced mechanism for model control beyond pre-training constraints.

Practically, such innovation can significantly benefit applications across different domains. For instance, summarization applications can be developed with customizable length outputs, tailored to specific user interfaces or content guidelines without retraining the model from scratch.

Future Directions

Looking towards future developments, this paper opens several avenues for further research, including exploring the application of these techniques in multi-modal settings or adapting these length-control methods to other types of recurrent neural networks or transformer architectures. Furthermore, experiments could be broadened to incorporate domain-specific constraints, potentially refining the applicability to specialized fields such as medical or legal document summarization.

In conclusion, the paper provides a comprehensive assessment of methods to control output length in neural encoder-decoder architectures, offering valuable insights and practical tools to the field of natural language processing. The proposed methods establish a reference point for future exploration aimed at enhancing sequence generation models' flexibility and performance.